Tracking in Haptic Systems

ABSTRACT

Described herein are techniques for tracking objects (including human body parts such as a hand), namely: 1) two-state transducer interpolation in acoustic phased-arrays; 2) modulation techniques in acoustic phased-arrays; 3) fast acoustic full matrix capture during haptic effects; 4) time-of-flight depth sensor fusion system; 5) phase modulated spherical wave-fronts in acoustic phased-arrays; 6) long wavelength phase modulation of acoustic field for location and tracking; and 7) camera calibration through ultrasonic range sensing.

RELATED APPLICATION

This application claims the benefit of seven U.S. Provisional PatentApplications, each of which is incorporated by reference in itsentirety:

-   1) Ser. No. 62/609,576, filed on Dec. 22, 2017;-   2) Ser. No. 62/776,209, filed on Dec. 6, 2018;-   3) Ser. No. 62/776,274, filed on Dec. 6, 2018;-   4) Ser. No. 62/776,439, filed on Dec. 6, 2018;-   5) Ser. No. 62/776,449, filed on Dec. 6, 2018;-   6) Ser. No. 62/776,457, filed on Dec. 6, 2018; and-   7) Ser. No. 62/776,554, filed on Dec. 7, 2018.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to improved techniques fortracking objects and human body parts, such as hands, in haptic systems.

BACKGROUND

A continuous distribution of sound energy, which will be referred to asan “acoustic field”, can be used for a range of applications, includingparametric audio, haptic feedback in mid-air and the levitation ofobjects. By defining one or more control points in space, the acousticfield can be controlled. Each point can be assigned a value equating toa desired amplitude at the control point. A physical set of transducerscan then be controlled to create an acoustic field exhibiting thedesired amplitude at the control points.

By changing the amplitude and/or the phase angle at the control points,a variety of different effects can be produced to create hapticfeedback, levitate objects or produce audible sound. Consider a hapticfeedback system as an example scenario. Haptic feedback is generated byan array of transducers, and a user's gesture is recognized by means ofan optical camera. By recognizing the user's gesture, an action isperformed, and a different haptic feedback is provided as a response.

An effective and elegant way of performing the hand tracking and thegesture recognition, while providing a haptic feedback to the user (orin general producing an acoustic field), can be achieved exclusivelyusing sound energy. This technique makes use of a transducer outputobtained as an interpolation of the transducers' state between aplane-wave state and a focused-wave state. With this solution, for halfof the time the transducers move towards a plane wave state, when thehand tracking is performed exploiting the modulated feature of thereflected signals, and a focused-wave state, when the haptic feedback isgenerated in mid-air. The tracking signal may be implemented in practiceas modulation by amplitude, phase and frequency. The tracking waveformshould be distinct in frequency components and/or a signal made up ofsuitably orthogonal functions so that it may be picked out of the mix offrequencies expressed at the control point. These signals would bereflected from objects in the field allowing existing echo processingtechniques to perform tracking.

Further, by controlling the amplitude and phase angle of an acousticfield, a variety of different effects can be produced (e.g. creatinghaptics feedback, levitate objects, produce audible sound, tractorbeaming objects). The generation of effective haptic feedback, on top ofkeeping the audible noise low, as well as any other further requirementsat once, is not trivial even with complete control over these andtherefore techniques and methods that can achieve this are valuable.

Further, phase singularities may be introduced into a largelymonochromatic ultrasonic wave in order to determine the time-of-flight(ToF) by detecting a reflection of the phase change and thus calculatingwhen and where the phase singularity originated. This has previouslybeen shown by focusing a phase singularity to coincide at a point andthen measuring the reflected response from this location to determine adistance from a flat array.

Further, accurate and fast 3D scene analysis and hand gesturerecognition are essential tasks for many applications in computergraphics, ranging from human-machine interaction for gaming andentertainment and virtual and augmented reality, to industrial andhealthcare, automotive, object tracking and robotics applications. Asexample scenarios, 3D geometrical information of real environment couldbe used to remotely control the full movements of a humanized robot, orto receive haptic feedbacks onto the bare skin, as it happens withhaptic feedback systems.

This challenge is typically tackled by the computer vision community,exploiting the propagation of electromagnetic waves in the range of400-1000 nm (i.e. both the visible and invisible infrared spectra) bymeans of optical systems.

Further, by changing the amplitude and/or the phase angle at the controlpoints, a variety of different effects can be produced to create hapticfeedback, levitate objects or produce audible sound. Consider a hapticfeedback system as an example scenario. Haptic feedback is generated byfocused ultrasonic waves, and a user's gesture is recognized by means ofan optical camera. By recognizing the user's gesture, an action isperformed, and a different haptic feedback is provided as a response.

An effective and elegant way of performing the hand tracking, whileproviding a haptic feedback to the user (or in general producing thedesired acoustic field), can be achieved exclusively using sound energy.The generated acoustic field may consist of phase modulated sphericalwave-fronts. Inserting phase shifts in the in-phase carrier frequency ofeach transducers of a 2D array, in such a manner to make them collide ata focus, yields the generation of spherical wave-fronts with differentphases, within a multitude of different amplitude modulated wave-fronts.The tracking system exploits the benefit of having a spherical spreadingwave-front, as opposed to acoustic amplitude and phase beaming. Thetracking waveform should be a signal made up of suitably orthogonalfunctions so that it may be picked at receivers' locations. Thesesignals would be reflected from objects in the field allowing existingecho processing techniques such as multilateration to perform tracking.

A different, existing solution to the stated problem of producing anacoustic field with known features and simultaneously using trackingsystems was introduced in the US Application patent US/2017 0193768A1“Calibration and Detection Techniques in Haptic Systems”, section IV,where the concept of “virtual acoustic point source” was described forthe first time. The “virtual acoustic point source” is generated bybeaming amplitude and phase inversions at a focus. In fact, quotingliterally: “These sources would be reflected from objects in the fieldallowing existing sonar, range-finding and acoustic imaging techniquesto function by applying a filter to received signals such that only thetracking signals are recovered. These tracking signals may beimplemented in practice as modulation by amplitude, phase, frequency orquadrature, so long as this achieves a resulting modulation thatsubstantially fits within bands of acoustic frequencies above the rangeof human hearing. Alternatively, the tracking signal may be audible, butdesigned to be unobtrusive in audible frequencies, which could beachieved by designing it to have similar properties to a random noisefunction. The tracking waveform associated with each control pointshould be distinct in frequency components and/or a signal made up ofsuitably orthogonal functions so that it may be picked out of the mix offrequencies expressed at the control point. Using further frequencies ontop of each control point allows the tracking to continue to functioneven during periods of device activity.”

Another attempt to address the problem of tracking and producing hapticsat the same time is where the signal emitted from the transducers arraywould be a combination of a plane-wave state, in which a tracking signalcould be encoded, and of a focused state, in which the acoustic field iscontrolled in the wanted manner to produce the haptic sensation. Theconcept of state interpolation is extended even further to include thepossibility to interpolate between “n” states.

Further, a machine may be made to respond or react appropriately to auser's commands expressed as dynamic gestures of the hand, or else asstatic gestures such as placing one's hand in specific locations withina volume. An essential component of this capability is for the machineto be able to locate and track an object within the same volume.

Specifically, one example scenario of human-computer interface would bethe use of a haptic feedback system, in which an acoustic fieldgenerates haptic sensations as a way to communicate information to auser. Furthermore, the system also tracks the user's hand and interpretsthe movements as gestures to communicate information from the user tothe computer.

Furthermore, tracking a user's hand while also providing reasonablehaptic sensations to the same hand using an acoustic field and withoutinterruption adds to the challenge; conventional ranging techniques aredeemed unsuitable as they would require significant interruption to thehaptic sensations.

Given that the system is providing haptic sensations using an acousticfield, then using technologies other than acoustic excitation andreception for the location and tracking of the user's hand in the volumeadds to the cost and complexity of the final implementation. A low costand reliable technique is sought for locating a user's hand within avolume.

The location of an object may be determined in a number of ways usingacoustic fields. One such method includes generation of an acousticfield that is transient in nature, for example where an acoustic pulseis transmitted into a volume and the reflection monitored. The timetaken for the reflection to arrive from the transmission time determinesthe distance of the object within the volume. Multiple transmissions andmultiple receivers could be utilized to determine the location of theobject in three dimensions. The use of transient pulses implies that themeasurements can be made only at quantized time intervals that arespaced out in time to allow the excitation to travel from the emitter,to the object and then back again. This fundamentally limits the maximumupdate rate of the system to be the ratio of the distance betweenemitter and reflector to the relatively slow speed of sound.

A further restriction is that while generating haptic sensations, it isundesirable to interrupt the haptic sensation generation in order totransmit and receive a ranging pulse as this would likely interfere withor diminish the haptic sensations.

In order to avoid disruption of the haptic experience, it isadvantageous to use a method for ranging or location that is orthogonalto the haptic generation features. One such example is to encode theranging pulse into the phase of the generated acoustic phase. A phasestep applied to all or some of the emitting transducers does notinterfere with the haptics, and the phase step can be demodulated afterreceiving the reflected pulse in order to determine the distance of thereflector. Multiple transmitters and receivers may be utilized todetermine the location in three dimensions. Once again, this is based ona transient ranging technique and is thus significantly limited in themaximum update rate due to the time taken for sound to complete thejourney.

It is important in such transient techniques to allow separation in timebetween adjacent ranging pulses to complete the journey, otherwise thereceiver is unable to differentiate between them and therefore cannotdetermine the location of the reflector unambiguously.

Avoiding the use of transient features in the acoustic field, one couldconsider comparing the phase of the received acoustic wave with that ofthe transmitted wave. The frequency of the acoustic wave used should beoutside of the audible range in order for it to be used with comfort,and so this means using either subsonic frequencies, for example around1 Hz, or else ultrasonic frequencies, for example greater than 30 kHz.

Using subsonic frequencies means that the sensitivity of the systemwould be low, requiring disproportionate cost to implement withsufficiently high fidelity as to resolve small changes in phase of asubsonic wavelength for reasonable changes in physical displacement of areflector. In real systems, the natural noise in the implementation islikely to be a significant challenge to attain the fidelity, orsignal-to-noise ratio, required to estimate small changes in distanceaccurately.

Using ultrasonic frequencies can be equally challenging in differentareas. For example, the system becomes too sensitive, delivering a highrate of change of phase difference for small changes in physicaldisplacement. This is due to the short wavelength. For example, for anacoustic wavelength of 1 cm, then the phase comparison would wrap aroundthe 2 Pi limit when the reflector moves 0.5 cm since the wave musttravel to the reflector and then back again to the receiver. Given this,it becomes difficult, if not impossible, to locate a reflector that ismore than half a wavelength away from transmitter and receiver.Furthermore, if a reflector moves more than half a wavelength betweenadjacent measurements then the system cannot determine the locationwithout ambiguity and without significant cost and complexity ofimplementation. The practical utility of comparing the phase of thereceived wave to that of the wave being transmitted diminishes rapidlywith increasing acoustic wave frequency and thus the systems isultimately less reliable and less accurate.

Further, a 3D depth sensor system may operate based on brightnessoptimization collected with one single optical camera. The brightness oftracking objects is related to its range via an optimization algorithm,which is constantly calibrated exploiting the ground truth obtained withultrasonic, time-of-flight measurements.

SUMMARY

Controlling an acoustic field while performing tracking of an object isoften needed in many applications, like in haptic feedback systems.

Tracking signal can be implemented in practice by modulation ofamplitude, phase and frequency, so to be distinct in frequencycomponents and/or made up of suitably orthogonal functions. The signalemitted from the transducer would be a combination of a plane wavestate, in which the tracking signal would be encoded, and of a focusedstate, in which the acoustic field is controlled in the wanted manner.The tracking signals would be reflected from objects in the fieldallowing existing echo processing techniques to perform tracking.

Further, by controlling the amplitude and phase angle of an acousticfield, a variety of different effects can be produced. The generation ofeffective haptic feedback, on top of keeping the audible noise low, aswell as any other further requirements at once is not trivial even withcomplete control over these and therefore techniques and methods thatcan achieve this are valuable. Various modulation techniques aresuitable for generating the desired acoustic field by controlling phaseand amplitude, while keeping the audible noise low or performing othertasks, like tracking of objects.

Further, by electrically monitoring the transducer and throughforeknowledge of the transducer transient response, the output pushedthrough the circuitry may be deconvolved and subtracted from theelectrical behavior, leaving only the interactions of the reflectedwaves with the transducers.

Further, a time-of-flight sensor fusion system for depth and rangesensing of objects is achieved with the integration of multiple datacoming from embedded acoustic and optical sensors. The expenses requiredto process the acoustic and optical data is intended to be very low andto happen on-chip, in order to intelligently eliminate as much of theexpensive bandwidth that common tracking cameras share. The integrationand fusion of different data eventually define a tracking and gesturerecognition system with fast response time, low latency, medium range,low power consumption, mm-level accuracy and low build cost.

Further, tracking signal can be implemented in practice by modulation ofphase, so to be made up of suitably orthogonal functions. Insertingphase shifts in the in-phase carrier frequency of each transducers of a2D array, in such a manner to make them collide at a focus, yields thegeneration of spherical phase modulated wave-fronts, within different(focused, spherical and in-phase) amplitude modulated wave-front. Thetracking system described herein exploits the benefit of having aspherical spreading wave-front, as opposed to beamforming techniques.The tracking signals of the spherical wave-front would be reflected fromobjects in the field allowing existing echo processing techniques suchas multilateration to perform tracking.

Further, a system of locating and tracking an object using an acousticfield from a transducer array is presented here. The system or method isorthogonal to the method used to generate haptic sensations. Therefore,the location and tracking proceeds while also providing uninterruptedhaptic sensations from the same transducer array. The system allows forvariable sensitivity to physical displacement, does not generate audiblesound and allows location and tracking at a high rate which isindependent of the range and speed of sound. Utilizing a long wavelengthalso allows a sparse receiver population in the transducer array, whichboth reduces cost of the location implementation and also maintains ahigh density of emitter transducers which is important for fieldgeneration for haptic feedback. Augmentations to the basic system arepossible, for example varying the sensitivity to physical displacementin real time or spatial coding to use different sensitivities fordifferent regions of the array and doing so in real time to respond tothe environment, the object's position or speed. The system may beintegrated into the same system that is used to generate the hapticsensations if required for reduced implementation complexity and cost.The sensitivity of the system to physical displacement may be calibratedor tuned to requirements through adjustment of the wavelength, orwavelengths in the case of spatial coding. An algorithm, data patharchitecture and implementation of such a technique are presented.

Further, a 3D depth sensor system based on brightness optimizationcollected with one single optical camera is presented. The brightness oftracking objects is related to its range via an optimization algorithm,which is constantly calibrated exploiting the ground truth obtained withultrasonic, time-of-flight measurements.

BRIEF DESCRIPTION OF THE FIGURES

The accompanying figures, where like reference numerals refer toidentical or functionally similar elements throughout the separateviews, together with the detailed description below, are incorporated inand form part of the specification, serve to further illustrateembodiments of concepts that include the claimed invention and explainvarious principles and advantages of those embodiments.

FIGS. 1A, 1B and 1C are graphs of transducer output.

FIG. 2 is a graph of transducer output.

FIGS. 3A and 3B are video snapshots of a numerical simulation.

FIGS. 4A, 4B, 4C, 4D, 4E and 4F are comparisons modulations on a controlpoint.

FIG. 5 is an output signal of two transducers.

FIG. 6 is an output signal of upper envelope signals.

FIGS. 7A and 7B are video snapshots of a numerical simulation.

FIG. 8 is a schematic of a stereo-vision method.

FIG. 9 is a block diagram of a synchronous demodulator.

FIG. 10 is a trilateration schematic.

FIGS. 11, 12, 13, 14 and 15 are video rate hand tracking images.

FIG. 16 is a flowchart of a sensor fusion systems.

FIG. 17 is a set of graphs that show signals emitted by 5 transducers.

FIGS. 18A and 18B are video snapshots of a numerical simulation.

FIG. 19 is a data path schematic.

FIG. 20 is a graph showing the magnitude spectra of two signals.

FIGS. 21, 22 and 23 are graphs showing reference modulation components.

FIGS. 24, 25 and 26 are graphs showing receiver modulation components.

FIGS. 27 and 28 are graphs showing receiver phase on modulationcomponents.

FIG. 29 is a 3D depth sensor system.

FIG. 30 is a graph showing brightness versus depth.

FIGS. 31A and 31B are scatter plots of the goodness of fit for trainingsamples.

FIG. 32 is a flow chart of the 3D depth sensor system.

FIG. 33 is a graph showing brightness versus depth.

Skilled artisans will appreciate that elements in the figures areillustrated for simplicity and clarity and have not necessarily beendrawn to scale. For example, the dimensions of some of the elements inthe figures may be exaggerated relative to other elements to help toimprove understanding of embodiments of the present invention.

The apparatus and method components have been represented whereappropriate by conventional symbols in the drawings, showing only thosespecific details that are pertinent to understanding the embodiments ofthe present invention so as not to obscure the disclosure with detailsthat will be readily apparent to those of ordinary skill in the arthaving the benefit of the description herein.

DETAILED DESCRIPTION

(1). Two-State Transducer Interpolation in Acoustic Phased-Arrays

I. Two-State Transducer Interpolation with Phase Modulation

As previously disclosed, one way of tracking a user's hand is by meansof an optical camera. Introducing a phase modulation of the sinusoidalcontinuous waves enables the tracking of an object in mid-air bytime-of-flight estimations.

In the following example, a message is encoded in the sinusoidaltransmitted signal in the form of many abrupt phase shifts at knowninstants in time. A received signal is recorded and demodulated at someremote locations by means of receiving transducers. Introducing a phasemodulation into the transducers' signal allows receiving transducers ormicrophones to synchronize on the reflected signal, yielding the abilityto detect the distance of an object, such as the hand, from the array.

Ideally the transducers state would switch between a focused state,which as to control point activation, U_(f)(t):

U _(f)(t)=A sin(2πf _(c) t+θ+ϕ)  (1)

and a plane state, which as to plane wave activation, U_(p)(t):

U _(p)(t)=A sin(2πf _(c)+ϕ)  (2)

wherein A is the signal amplitude, f_(c) is the centre frequency, θ isthe phase delay added to the signal to activate the control point, and ϕis the phase shift modulation applied to the signal to achieve tracking.

But since output transducers have frequency dependent amplitude behaviordue to their frequency response curves, the amplitude output from thetransducer fluctuates when a phase shift is encoded into the signal whenphase modulation is applied. This sudden change in the output amplitude,which is usually in the form of a sharp attenuation, creates substantialaudible noise from the array of transmitting transducers.

One way to remove this substantial source of noise is to use thevariation in the signal; finding points in the amplitude modulation sothat the sudden change in amplitude induced by the phase changecoincides with the amplitude minimum. This would cause the signalsgenerated by a transducer in the focused state U_(f)(t) and in the planestate U_(p)(t), to be of the form:

U _(f)(t)=A sin(2πf _(c) t+θ+ϕ)·[1−cos(2πf _(m) t)]·M+(1−M)  (3)

and:

U _(p)(t)=A sin(2πf _(c) t+ϕ)·[1−cos(2πf _(m) t)]·M+(1−M)  (4)

wherein M is the modulation index and f_(m) is the modulation frequency.

Finally, the interpolation between the two different states is achievedas follows:

$\begin{matrix}{{U(t)} = {{\left\lbrack {1 - {\cos\left( {2\pi{f}_{m}t} \right)}} \right\rbrack \cdot \frac{U_{f}(t)}{2}} + {\left\lbrack {1 - 1 + {\cos\left( {2\pi f_{m}t} \right)}} \right\rbrack \cdot \frac{U_{p}(t)}{2}}}} & (5)\end{matrix}$

FIG. 1A shows an example of a transducer's output obtained as aninterpolation between the control point activation state and the planewave activation state. Specifically, FIG. 1A shows transducer outputU(t) as obtained from the analytical model described from equation (5),with A=1, M=80%, f_(c)=40 kHz, f_(m)=10 Hz, θ=3π/2 and ϕ=π. A graph 100has an x-axis 110 of time in seconds and a y-axis 120 of normalizedamplitude. The plot shows the transducer output 130.

Shown in FIG. 1B is the output of the transducer matches that of thecontrol point activation state at maxima, being totally uncorrelated tothat of the plane wave activation state. A graph 140 has an x-axis 145of time in seconds and a y-axis 150 of normalized amplitude. The plotshows the transducer output 155 and plane wave activation 165.

In the same way, FIG. 1C shows the matches that of the plane waveactivation state at minima (where the phase shift happen) are totallyuncorrelated to that of the control point activation state. A graph 170has an x-axis 175 of time in seconds and a y-axis 180 of normalizedamplitude. The plot shows the transducer output 185 and control pointactivation 190.

II. Arbitrary Waveforms Including Phase Modulation

Given an arbitrary waveform, n envelope detectors may be employed tofind the maxima and minima of an amplitude modulated waveform. Bymonitoring where the minima, the zero-crossings of the waveform signallie, phase shifts may be generated in these locations. Given an upperlimit to the frequency of the phase shifts employed, which effectivelycan be a minimum delay criterion, the last n phase shift events may beretained. Matching against this is then a matter of maintaining ncomparators, each contributing the likelihood of the hypothesis that thereflection of a phase shift is received at any given point in time. Bymaintaining these comparators, an arbitrary signal may be used toconduct this time-of-flight detection. This may be implemented such thatthe phase shift is encoded within an envelope of an ultrasonic carriermodulated with a signal intended to be parametric audio. In this and thegeneral case, the phase shifts can then be added without materiallymodifying the resulting signal.

III. Two-State Transducer Interpolation with Frequency Modulation

A similar way of simultaneously tracking an object in mid-air andproducing an amplitude modulated control point can be achieved byintroducing a frequency modulation of the plane wave state. In fact, asmall pulsed signal or chirp with distinct frequency components than thecontrol point activation state can be used to perform tracking, so thatit may be picked out of the mix of frequencies expressed at the controlpoint. The received signals are recorded at remote locations and thetime-of-flight recovered by means of standard cross-correlationalgorithms. Introducing a frequency modulation into the transducers'signal allows receiving transducers or microphones to synchronize on thereflected signal, yielding the ability to detect the distance of anobject, such as the hand, from the array.

Ideally the transducers state would switch between a focused state,which as to control point activation, U_(f)(t).

U _(f)(t)=A sin(2πf _(c) t+θ)  (6)

and a plane state, which as to plane wave activation, U_(p)(t).

U _(p)(t)=A sin(2πf _(t))  (7)

wherein A is the signal amplitude, f_(c) is the centre frequency of thecontrol point activation state, f_(t) is the centre frequency of theplane state and θ is the phase delay added to the signal to activate thecontrol point.

The aim is to interpolate the two different states such that for halfthe time the device is moving towards a plane wave state and for theother half toward a focused state. One way to achieve this result is touse the variation in the signal; finding points in the amplitudemodulation so that the amplitude maximum of plane wave state coincideswith the amplitude minimum of the control point state. This would causethe signals generated by a transducer in the control point stateU_(f)(t) and in the plane state U_(p)(t), to be of the form:

U _(f)(t)=A sin(2πf _(c) t+θ)·[1−cos(2πf _(m) t)]·M+(1−M)  (8)

and:

U _(p)(t)=A sin(2πf _(t) t)·[1−cos(2πf _(m) t+π)]·M+(1−M)  (9)

wherein M is the modulation index and f_(m) is the modulation frequency.

Finally, the interpolation between the two different states is achievedas follows:

$\begin{matrix}{{U(t)} = {{\left\lbrack {1 - {\cos\left( {{2\pi f_{m}t} + \pi} \right)}} \right\rbrack \cdot \frac{U_{f}(t)}{2}} + {\left\lbrack {1 + {\cos\left( {{2\pi f_{m}t} + \pi} \right)}} \right\rbrack \cdot \frac{U_{p}(t)}{2}}}} & (10)\end{matrix}$

As a result, the output of the transducer contributes to the formationof a control point for half the time, while projecting in-phase pulsedsignals or chirps for the other half of the time. Nonetheless, frequencymodulation could produce audible noise. Keeping the audible noise lowcan be achieved by reducing the amplitude of the plane wave stateactivation at a minimum detectable value, or dynamically adapt amplitudeand frequency of the modulation to match the tracker's requirements.

FIG. 2 shows an example of a transducer's output obtained as aninterpolation between the control point activation state and the planewave activation state, following equation (10). Specifically, FIG. 2shows a transducer output U(t) as obtained from the analytical modeldescribed by equation (10), with A=1, M=80%, f_(t)=30 kHz, f_(c)=40 kHz,f_(m)=500 Hz, θ=2.71π, superimposed to the corresponding control pointstate U_(f)(t) as obtained from equation (8), and the plane stateU_(p)(t) as obtained from equation (9). A graph 200 has an x-axis 210 oftime in seconds and a y-axis 220 of normalized amplitude. The plot showsthe plane wave activation 230, control point activation 240 andtransducer output 250.

FIGS. 3A and 3B show two snapshots from a video of a two-dimensionalnumerical simulation accomplished with a two-state frequency modulatedtransducer interpolation, with the parameters described for FIG. 2. FIG.3A is a simulation 300 with an x-axis of y-position in mm 310 anday-axis of x-position in mm 320 with a snapshot 330. FIG. 3B is asimulation 350 with an x-axis of y-position in mm 360 and a y-axis ofx-position in mm 370 with a snapshot 380. Specifically, the figures aresnapshots from the video of a numerical simulation accomplished with atwo-state frequency modulated transducer interpolation, showing theacoustic pressure wavefield for the plane wave activation state (FIG.3A) and for the control point activation state (FIG. 3B). A total of 16transducers were used in the simulation to create a central controlpoint at 0.20 in, a horizontal reflector being positioned at the samedistance from the array.

IV. Arbitrary Waveforms Tracked Via Autocorrelation

An arbitrary waveform, if delayed in time on a per transducer basis, maybe made to arrive simultaneously at the focus. By employingauto-correlation with the amplitude modulation at each receiver, thetime of flight may be recovered. The amplitude modulation may be formedby using an interpolation between a focused state, representing a highpoint in the modulated signal at the focus, and a plane wave thatgenerates a low root-mean-squared pressure at the focus. In this way, anarbitrary waveform may be used to track the object in space, withoutmodifying the amplitude modulated signal at the focus.

V. Tracking of the Object

As previously discussed, the introduction of phase and/or frequencymodulation into the transducers' signal yields the ability to detect thedistance of an object, such as the hand, from the array and controlpoint. Each receiver yields an estimation of the distance of the object.In case phase modulation is adopted, the signal that arrives at thereceiving location is a complicated analog waveform that needs to bedemodulated in order to recover the original message. The demodulationis accomplished through a standard process called ‘carrier recovery’which consists of figuring out both the frequency and phase of themodulating sinusoid.

In case frequency modulation is adopted, the time-of-flight is estimatedby adopting standard cross-correlation algorithms.

The phase/frequency modulation can be dynamically tailored to match thesensing objective and the environment.

The presence, location and distance of the reflector in space isrevealed once the time-of-flight is recovered. Moreover, if thereflector does not have a predominant dimension, atrilateration/multilateration process would reveal its approximateposition in the tri-dimensional space. At contrary, if the reflector hasa predominant dimension, it could be possible to trilaterate theequation of the plane of best approximation relative to an arbitrarycoordinate reference system in the tri-dimensional space.

VI. Additional Disclosure

Additional disclosures is set forth as follows:

1. A technique to interpolate two different transducer states to controlacoustic field and track objects.

1a. A method of paragraph 1 in which phase and amplitude are used tomodulate the tracking signal.

1b. A method of paragraph 1 in which amplitude and frequency are used tomodulate the tracking signal.

1c. A method of paragraph 1 in which the modulation parameters can bedynamically tailored to match the sensing objective and the environment.

1d. A method of paragraph 1 in which arbitrary waveforms are modulatedby interpolating between a focused state and a plane wave state in sucha way to constantly maximize amplitude at focus.

2. A technique in which arbitrary waveforms (e.g. intended to beparametric audio AM carrier wave) can be used to amplitude modulate thesignal, and phase shifts can be added at the minima of the amplitudemodulated signal.

(2). Modulation Techniques in Acoustic Phased-Arrays

I. Combining Amplitude and Phase Modulation

As previously disclosed, one way of creating haptic feedback is toamplitude modulate the carrier wave with an amplitude modulating signal.Introducing a phase modulation into the control point allows receivingtransducers or microphones to synchronize on the reflected signal,yielding the ability to detect the distance of an obstacle, such as thehand, from the array and control point. However, since outputtransducers have frequency dependent amplitude behavior due to theirfrequency response curves, the amplitude output from the transducerfluctuates when a phase shift is encoded into the signal when phasemodulation is applied. This sudden change in the output amplitude, whichis usually in the form of a sharp attenuation, creates substantialaudible noise from the array of transmitting transducers.

One way to remove this substantial source of noise is to use thevariation in the signal; finding points in the amplitude modulation sothat the sudden change in amplitude induced by the phase change mimics aportion of the already intended modulation signal. While again becauseof the nature of the transducer frequency response there should be aminimum time between shifts placed into the signal so that they may bedetected separately, these may be otherwise placed anywhere the signal.In some cases, traditional amplitude modulation may be replaced oraugmented by the addition of such phase shifts. In the case of a simplesine wave modulation and a transducer frequency response that causes anamplitude fall on the induced frequency shift, this can be simplyfinding the minimum portion of the signal and placing the phase shiftdirectly before it, causing the amplitude drop to coincide with theamplitude minimum. Microphone recordings of such an inserted phase shiftand comparisons to examples of a phase shift in a continuous carriersignal and a plain amplitude modulation are shown in FIGS. 4A-4F.

FIG. 4A shows a simulation 400 with an x-axis 402 of time in seconds anda y-axis 404 of amplitude in Pascals with a graph 405. FIG. 4B shows asimulation 410 with an x-axis 412 of time in seconds and a y-axis 414 ofamplitude in Pascals with a graph 415. FIG. 4C shows a simulation 420with an x-axis 422 of time in seconds and a y-axis 424 of amplitude inPascals with a graph 425. FIG. 4D shows a simulation 430 with an x-axis432 of time in seconds and a y-axis 434 of amplitude in Pascals with agraph 435. FIG. 4E shows a simulation 440 with an x-axis 442 of time inseconds and a y-axis 444 of amplitude in Pascals with a graph 445. FIG.4F shows a simulation 450 with an x-axis 452 of time in seconds and ay-axis 454 of amplitude in Pascals with a graph 455.

FIGS. 4A-4F are comparison of different 200 Hz modulations on a controlpoint above a transducer array recorded by a microphone. FIGS. 4A, 4B,4C show the output of the modulated signal over 50 milliseconds, whileFIGS. 4D, 4E, 4F show details of the transition over a window of 5milliseconds. FIG. 4A, 4D show the result of introducing phasemodulation to a flat carrier wave signal. The sharp changes in amplitudemake this approach produce considerable audible noise. FIGS. 4B, 4Eshows an amplitude modulated signal with a modulation index of 80%.FIGS. 4C, 4F show the same amplitude modulated signal with the phaseshift occupying the minimum amplitude point on the periodicallyrepeating amplitude modulation. As the decrease is much shallowercompared with the case shown on the top row, the amount of audible noisegenerated is greatly reduced.

II. ‘Haptic Chirp’—Frequency Modulation

A modulation at a single haptic frequency does not necessarily providethe most effective haptics for a control point. To convey roughness, avariety of different frequencies may be required. Potentially, a ‘hapticchirp’, a frequency modulation composed of different frequencies thatare in the band of frequencies that are detectable by skin, can bepresented by the mid-air haptic device. A simple way to modulate themodulation frequency is to use the canonical frequency modulationequation:

$\begin{matrix}{{g(t)} = {A{\cos\left( {{2\pi f_{c}t} + {\frac{f_{\Delta}}{f_{m}}{\sin\left( {2\pi f_{m}t} \right)}}} \right)}}} & (11)\end{matrix}$

wherein A is the signal amplitude, f_(c) is the centre frequency, f_(Δ)is the amplitude of the change in frequency and f_(m) is the frequencyat which the frequency modulation occurs. By applying phase shifts tothe frequency modulations, several different frequency modulations canbe applied at once as:

$\begin{matrix}{{g(t)} = {\sum_{p = 1}^{n}{A_{p}{\cos\left( {{2\pi f_{c,p}t} + {\frac{f_{\Delta,p}}{f_{m,p}}{\sin\left( {{2\pi f_{m,p}t} + \phi_{p}} \right)}} + \phi_{p}} \right)}}}} & (12)\end{matrix}$

yield the combination of multiple frequency modulation modulations.Further, to produce a feeling describable as “rough” a random continuoussignal h(t) may be produced to fill in for the sine in the frequencymodulation equation as:

$\begin{matrix}{{g(t)} = {\sum\limits_{p = 1}^{n}{A_{p}{\cos\left( {{2\pi f_{c,p}t} + {f_{\Delta,p}{h(t)}} + \phi_{p}} \right)}}}} & (13)\end{matrix}$

while ensuring that the frequency of modulation does not increase ordecrease beyond f_(Δ,p) by ensuring that the derivative of h(t) does notin absolute value exceed unity.

III. Direction of Particle Motion Modulation

When the system is solved for a directional particle speed, it ispossible to modify the direction of the particle speed optimized for intime. This generates a further class of modulation scheme that can beused to vary the direction of the acoustic radiation force generated, asit functions by changing the direction of the force vector. Changing thedirection of the force vector implies that when the force is generatedacross an unchanging, static normal vector, a changing force is producedwith respect to a static or slowly moving object, such as a hand. Thismodulation scheme, due to generating force changes in the air, may alsobe used to generate audible sound waves.

This technique may also be further used to stabilize or otherwise modifytrajectories of levitating particles by dynamically changing thedirection of the force. Further, by solving the optimization manythousands of times a second and using the results to apply the forcevectors obtained to an object or objects whose levitation is desired,these may be held in place without the traditional trapping mechanism ofa potential field. This has the advantage that less power is required asthe force is local, and instabilities can be corrected for, although afurther mechanism is required to track the positions and momenta of thelevitating objects in this case.

IV. “n”-Sided Modulation

Interpolating between a zero state and a state corresponding to amultiple valid control points that are amplitude modulated isinefficient, as for half of the time the device is moving towards thezero state in which nothing is output. As previously disclosed, becauseof this, using two states, one corresponding to one point and the othercorresponding to the other, are used alternatingly.

However, in the case that three control points are created, using twostates yields a set wherein two points share the resources provided bythe array, while the other has one point that can monopolies the array.This means that in situations in which the system is resourceconstrained and three points are presented as equal, two of the controlpoints are noticeably weaker, leading to a haptic effect that is not aseffective. To counter this, a three- or “n”-stage system is created. Asa result, the number of control points per state is more equal, yieldingand equal distribution of array power. This can be achieved by combiningsine waves exhibited by each control point or by cosine interpolationbetween control point states. Further, this does not have to produce anequal number of control points in each state, it is merely more equal,so it is possible to halt at some “n” and not have the control points beentirely equal.

In the limit, this means some m control points are factored into nstates. To choose which control points go into which states, controlpoints are selected close to each other so that they can take advantageof constructive interference. Also, states with control points next toeach other should be next to each other in time. To achieve thesplitting of the control point system, determine the spatial componentwith the least variation in control point position. Then, using thisaxis as a normal vector, count angle from an arbitrary starting point ineither direction, assigning control points with increasing angle to thefirst state, filling each with an appropriate integer number beforemoving onto the next, making each as close to evenly distributed aspossible. In this way, spatial closeness can be achieved when cyclingthe actuated states in numerical order.

Another advantage of this approach wherein multiple states areinterpolated between in sequence is that these states may be limited toonly one control point. In this case, the calculation required to createthe state is limited to not require the linear system solution neededwhen multiple points occupy the same state. In this manner, a devicewith greatly reduced computational requirements may be produced to lowercost and create a more competitive device.

V. Focused Amplitude Modulation in Phased-Arrays

Consider a haptic feedback system as an example scenario. Whengenerating the haptic effects, a focused control point in space ismodulated with a low frequency vibration, usually consisting of one ormore frequencies ranging from 0 Hz up to 500 Hz order to provide hapticfeedback in the case of an amplitude modulated point. The phase andamplitude of the modulation frequency is usually not controlled. Thiscauses the amplitude at control point to slightly blur and not beingoptimized. Nonetheless, this effect is negligible for the hapticfeedback to be perceived by humans when the length of the phased-arrayis smaller than half the wavelength of the amplitude modulationfrequency. Introducing a focused amplitude modulation to create virtualacoustic point sources in mid-air and to optimize the amplitude ofcontrol points regardless of the size of the device, can be achieved.

These sources would be reflected from objects in the field allowingexisting sonar, range-finding and acoustic imaging techniques tofunction by applying a filter to received signals such that only thetracking signals are recovered. Specifically, an amplitude demodulationtechnique such as an envelope detector, could be used to determine ToF,i.e. the time that it takes for an object, particle or acoustic,electromagnetic or other wave to travel a distance through a medium.Also, necessary to determine ToF is to monitor the delta time fromemission to the moment of focusing in order to correctly find when thetracking signal is ‘emitted’ from the virtual source in the controlpoint. From that point, the virtual source position, timings and emittedwaves are known, and so traditional techniques for determining theposition of virtual sources to one or many receivers may be used totriangulate reflections and image the space. The amplitude modulationcan be dynamically tailored to match the sensing objective and theenvironment.

The results of a two-dimensional numerical simulation showed that it ispossible to use a virtual acoustic point source created with amplitudemodulation, to track the distance of a horizontal reflector positionedat 0.20 m. FIG. 5 is the output signal of two transducers belonging to a2-D phased array of 16 transducers spaced 0.01 m each other, fromnumerical simulation. The carrier frequency was a 40 kHz sine wave, themodulating frequency was a 500 Hz sine wave and the modulation index was0.8.

FIG. 5 shows an output signal graph 500 having an x-axis of time inseconds 510, a y-axis of normalized amplitude 520, a plot showing theoutput signal of transducer 1 530 and a plot showing the output signalof transducer 8 540. As can be perceived from the figure, the phases andamplitudes of the both carrier and the modulating frequencies werecontrolled to produce a central control point at 0.20 m.

Reflected signal recorded at remote locations yields the ability todetect the distance of the reflector. The ToF may be determined with anenvelope detector technique. An example of the upper envelope of thereference signal and of two signals received at different transducerspositions is shown in FIG. 6, which shows the upper envelope of thereference signal and of two signals received at different transducerspositions from numerical simulation. FIG. 6 shows an output signal graph600 having an x-axis of time in seconds 510, a y-axis of normalizedamplitude 520, a plot showing the upper envelope reference signal forreceiver 1 630, a plot showing the upper envelope receiver signal forreceiver 1 640 and a plot showing the upper envelope receiver signal forreceiver 8 650.

ToF can be estimated from the maxima or minima of the envelopes. FIGS.7A and 7B show the acoustic pressure field as obtained from atwo-dimensional numerical simulation. FIG. 7A is a simulation 700 withan x-axis of y-position in mm 710 and a y-axis of x-position in mm 720with a snapshot 730. FIG. 7B is a simulation 750 with an x-axis ofy-position in mm 760 and a y-axis of x-position in mm 770 with asnapshot 780. This shows the acoustic wavefield when the maxima (FIG.7A) and the minima (FIG. 7B) of the amplitude modulated waveformscollide at the focal point.

VI. Additional Disclosure

A method to combine amplitude and phase modulation such that phaseshifts are added at the minima of the amplitude modulated signal tominimize audible noise.

A method to generate haptic chirps.

A “n”-stage system in which multiple states are interpolated between insequence.

A method to focus the amplitude modulation for optimization and trackingpurposes.

A method to dynamically change the direction of the force vector.

(3). Method for Fast Acoustic Full Matrix Capture During thePresentation of Haptic Effects

Full Matrix Capture (FMC) can be used to reconstruct completely anacoustic image of a three-dimensional scene by sending pulses (Diracdelta functions) from a series of transducers and using the same set oftransducers to receive each individual pulse. To use the techniquehowever, the transducers must be inactive to create a pulse. Further, ina naïve experimental set up, transducers may not send and receive at thesame time.

However, by electrically monitoring the transducer and throughforeknowledge of the transducer transient response, the output pushedthrough the circuitry may be deconvolved and subtracted from theelectrical behavior, leaving only the interactions of the reflectedwaves with the transducers. This is a standard method in acousticimaging techniques to obtain results for the full matrix when theinitially pulsed transducer may continue to ring as the reflected waveinteracts with it.

Abstracting this further, a continuously actuated transducer may be usedto receive, assuming some history and the current output signal isknown. This is useful in the case of haptics especially, as if hapticsis produced simultaneously, there is no break in the output in which toinsert a pulse.

A Gold code, or any auto-correlation maximization function (such as a deBruijn sequence) may be used to track a n-ary sequence of output symbols(although this may be restricted to binary). In wave multilaterationtechnologies, such as the global positioning system and others this maybe used to guarantee knowledge of the receiver's position in the inputsequence in time.

A Dirac delta function may be reconstructed in the reflected time seriesby taking a known input signal and deconvolving it from the receivedsignal. Since support is required through all frequencies and thetransducers are largely monochromatic in nature, the optimal approachmust have a similar frequency spectrum spread to the Dirac delta to aidfor example, a Wiener filter.

A phase singularity fulfils this requirement, as the phase shift spreadsenergy across all frequencies in a way that is similar in behavior tothe Dirac delta. In the creation of haptic effects, phase jumps may beincorporated into some of the transducers in the basis functions of thehaptic region and/or point sets. In order to create equivalent waves tothe Dirac deltas involved in the Full Matrix Capture technique.

The main problem with this approach is that introducing a phasesingularity into each transducer causes it to work against the othertransducers contributing to the focusing or control region behavior thathas been prescribed to create the haptic effects. To ameliorate thisissue, the concept of restitution must be introduced. Each transducer ismoved instantly by a large phase shift to generate the singularity pulsethat is recovered by the Full Matrix Capture method. Afterwards arestitution effect is applied to slowly pull the transducer back intoline with the other transducers in the system by moving the phase backslowly to the phase shift that it expressed before the singularity wasintroduced. As the number of transducers is large, over enoughtransducers in the system this would allow the phase shifting incurredto be negligible.

The other issue with the system so far described is that it is slow intime. In the traditional approach, the waves must be allowed tocompletely traverse the system before the next singularity or Diracdelta may be applied. To speed this up, a sequence of auto-correlationmaximization symbols are encoded into the transducer phase shiftsingularities to track them in both time and space. This may be assimple as assigning a symbol from the sequence uniquely to eachtransducer. In this way, a Hilbert curve or other localitymaximizing/space minimizing path may be used. This allows the bonding ofthe time between symbols and enables the use of a continuous set ofsymbols with a relatively small number of wave periods separation.Equally, if a de Bruijn sequence is used, a known minimum number ofsuccessful consecutive symbol detections may be obtained before thelocation in the space-time sequence is known. This is especially usefulif many of the symbols are missing due to the signals being too weak todetect and thus use. The locality is also useful as it is known that thesignal strength depends on space, meaning that groups of nearby symbolsare more likely to be received correctly if the sequence of transducerswhere singularities are introduced are close to each other.

By adding the phase shifts to the basis functions directly and followingHilbert curves to send the phase inversion pulses with encoded symbolsin phase shift keying, it is possible to create haptics which areminimally changed (as the effect of the phase shifts are knownbeforehand) while at the same time supporting the creation and detectionof pulses which amount to a real-time implementation of Full MatrixCapture. It is intended that with an example set of two hundredtransducers, with an inter-symbol distance of four wavelengths apart, at40 kHz, may receive and potentially process a full acoustic image fromthe scene in air at 50 Hz. This allows such a system to be competitivewith other imaging techniques at the resolution denoted by thewavelength in air. The technique would scale equivalently to differentfrequencies with potentially different number of transducers. It shouldalso be noted that multiple transducers at higher frequencies or inhigher numbers in the array may be grouped to produce a phase inversionsin tandem.

It should also be noted that some symbols may be missed due to weakness.In this case, the matrix entries in the Full Matrix Capture techniquemay be zeroed.

It should also be noted that the symbols may be redistributed in thecase that transducers are found to be inoperable.

(4). Time-of-Flight Depth Sensor Fusion System

There are several camera-based techniques in the literature to measurerange and depth. These include triangulation systems (such asstereo-vision), interferometry and time-of-flight systems.

Triangulation systems measure the distance of objects by analyzing thegeometrical features of triangles obtained by the projection of lightrays. In fact, given a point on the surface of the target, triangulationdetermines the angles α1 and α2 formed by the projection rays betweenthe surface point and the projection on the optical system. By knowingthe baseline, trigonometry yields the distance between the baselineitself and the surface point.

FIG. 8 shows a schematic 800 of a stereo-vision method of triangulation.Optical system 1 810 having a projection ray 1 840 and optical system820 having a projection ray 2 850 are positioned on a baseline. Bothprojection ray 1 840 and projection ray 2 850 are aimed a target 830.

Triangulation can be passive and active. In passive triangulation, thesame point is observed by two different optical components with knownbaseline distance. It is often recalled with the name stereo-vision, orstereo-triangulation, due to the use of two cameras. A full 3Drealization with stereo-vision is possible by solving the correspondenceproblem, in which features in both images are found and compared,typically using 2D cross-correlation. Off-the-shelf systems like “LeapMotion”, belong to this category. Active triangulation consists in astructured light emitter and an optical system. To apply triangulation,the light emitter should be well differentiated from other objects andambient light. This is achieved by projecting different coding schemesonto the 3D scene, typically colored, temporal (lines), spatial (randomtexture) and modulated schemes. Particularly. Kinect uses an infra-redlaser that passes through a diffraction grating, to create a structuredrandom pattern. This way, the matching between the infrared image andthe projection on the optical camera becomes straightforward.

Interferometry exploits the principle of superposition to combinemonochromatic waves, resulting in another monochromatic wave that hassome meaningful properties. Typically, a single beam of light is splitinto two identical beams by a beam splitter: while one ray is projectedto a mirror with a constant path length, the other beam is targeted onan object with variable path length. Both beams are then reflected tothe beam splitter and projected onto an integrating detector. By lookingat the intensity of the incoming wave it is possible to figure out thedistance of the target object, as the two split beams would interactconstructively or destructively. Interferometry is usually applied forhigh-accuracy measurements.

Time-of-flight systems are based on the measurements of the time that alight pulse requires to travel the distance from the target to thedetector. There are two main approaches currently utilized in ToFtechnology: intensity modulation and optical shutter technology.Off-the-shelf optical systems by former “Canesta”, former “MESA imaging”(now “Heptagon”), “Texas Instruments” and “PMDTec/lfm”, are all based onintensity modulation ToF. Its principle is based on the computation ofthe phase between the transmitted amplitude modulated, or pulsemodulated, optical signal and the incident optical signal, using samplesof the correlation function at selective temporal positions, usuallyobtained by integration. Phase is then translated to distance.Computation of time-of-flight happens at the CMOS pixel array level, butmaterial and build cost increase with respect to stereo-vision systems.Optical shutter technology, used by former “Zcam” in the early 2000s, isbased on fast switching off the illumination, obtained withlight-emitting diodes (LEDs), and on gating the intensity of thereceived signal with a fast shutter, blocking the incoming light. Thecollected light, at each pixel level, is inversely proportional to thedepth.

While all the aforementioned techniques achieve a full real time 3Dtracking of objects with various degrees of depth accuracy andinteraction areas, they often require expensive processing to happen onexternal processor, requiring the shuttle of big bandwidth of data.Also, software complexity and build and material cost is often high.

A time-of-flight depth sensor fusion system, which consists in thecombinatory use of electromagnetic (visible and non-visible lightspectrum) and acoustic waves to perform the complete 3D characterizationof a moving target, may be used. While a physical set of transducers (upto potentially only one) can be controlled to create an acoustic fieldwith desired phase and amplitude, and the depth of the target estimatedvia time-of-flight techniques, one or more optical cameras perform the2D tracking with respect to his projected plane, in the spatiallyperpendicular degrees of freedom. Ideally, this would yield a set oflocations, each of which is expressed in terms of (x, y, z) coordinateswith respect to an arbitrarily chosen reference system, corresponding torelevant features of the tracked target. In haptic feedback systems,this enables feedback to be projected to targetable locations.

The described tracking system would compete with other off-the-shelf,time-of-flight and depth cameras, as the tracking system is intended tobe included in a cheap embedded system, hence bringing down costs. Infact, off-the-shelf existing systems shuttle the relatively large videodata to be processed externally. Bringing the processing on-chip wouldmaintain software complexity low, while maintaining low build cost, lowlatency and high accuracy of tracking.

Section I introduces the principle and techniques to estimate theposition of a target with time-of-flight, acoustic techniques, with afocus on hand detection. Section II introduces the optical trackingsystem for hand detection. Finally, section III draws some conclusion onthe fusion system and its applications.

I. Acoustic Tracking System

The acoustic tracking is based on the measurement of ToF, i.e. the timethat an acoustic signal requires to travel the distance that separatesthe target from the receiver.

The acoustic tracking system consists of a set of transducers (up topossibly only one). They could be part of integrated haptic feedback,parametric audio, levitation systems, or a stand-alone tracking systemsupporting other applications. They could work simultaneously asemitters/receivers, or have independent, fixed tasks.

Usually, the emitted signal is a monochromatic, sinusoidal or squarewave, modulated with amplitude, frequency or phase modulation or acombination of those. In case a modulation of some kind is adopted, thesignal that arrives at the receiving location is a complicated analogwaveform that needs to be demodulated in order to extract the ToFinformation. This is accomplished through a standard process called‘carrier recovery’, which consists of figuring out both the frequencyand phase of the modulating sinusoid. The ToF information is thenusually recovered by clever integration. Spatial modulation and temporalmodulation could coexist to scan portion of the 3D space at time.Spatial modulation can be achieved in much the same way temporalmodulation is applied: different portion of the 2D array would projectsignal modulated differently.

Alternatively, the emitted signal can be broadband, containing more thanone frequency component. The ToF is usually recovered using narrowbandmethods on broadband signals, using Fast Fourier Transform (FFT),extracting the phase and amplitude of different sinusoids, or by meansof the cross-correlation function.

On a 2D array of transducer, in which each of the transducers have theability to both transmit and receive, ToF technique can be applied inmuch the same way it is applied for ToF cameras, where each transduceris the analogous of each pixel, to obtain a full acoustic image ofrange. If only a limited number of receivers in the 3D space isavailable, a full acoustic imaging of the target is impossible. Thelatter can be treated as a virtual source of reflected waves, allowingtechniques like trilateration, multilateration or methods based onhyperbolic position location estimators, to estimate the position of thevirtual source. Moreover, methods based on the parametric andnon-parametric estimation of the direction of arrival (DoA), likeconventional beamforming techniques, the Capon's method and the MUSICalgorithm, can be used to further constrain the position of the targetsince they give information about the bearing of the source.

In a haptic feedback system, a physical set of transducers can becontrolled to create an acoustic field exhibiting the desired amplitudeat the control points. Acoustic tracking of the bare hand can beperformed while providing haptic feedback. An elegant way of doing it isachieved with the adoption of virtual acoustic point sources. In fact,quoting literally: “These sources would be reflected from objects in thefield allowing existing sonar, range-finding and acoustic imagingtechniques to function by applying a filter to received signals suchthat only the tracking signals are recovered. These tracking signals maybe implemented in practice as modulation by amplitude, phase, frequencyor quadrature, so long as this achieves a resulting modulation thatsubstantially fits within bands of acoustic frequencies above the rangeof human hearing. Alternatively, the tracking signal may be audible, butdesigned to be unobtrusive in audible frequencies, which could beachieved by designing it to have similar properties to a random noisefunction. The tracking waveform associated with each control pointshould be distinct in frequency components and/or a signal made up ofsuitably orthogonal functions so that it may be picked out of the mix offrequencies expressed at the control point. Using further frequencies ontop of each control point allows the tracking to continue to functioneven during periods of device activity.”

These techniques yield the estimation of the range of the center of massof the target (i.e. the palm of the bare hand) with respect to the arrayof transducer, and possibly its location in the spatially perpendiculardegrees of freedom.

Section A describes the processing necessary to recover ToF from a phasemodulated, acoustic signal. Section B introduces some methods utilizedfor source location estimation from ToF measurements. Finally, section Cintroduces some direction of arrival (“DoA”) techniques that can be usedto further constrain the source location.

A. Modulation and Demodulation

Phase Shift Keying (PSK) is a digital modulation technique which conveysdata by changing the phase of the carrier wave. Binary Phase ShiftKeying technique (BPSK) is a type of digital phase modulation techniquewhich conveys data by changing the phase of the carrier wave by 180degrees. Quadrature Phase Shift Keying (QPSK) is another type of digitalphase modulation in which the modulation occurs by varying the phase oftwo orthogonal basis functions, which are eventually superimposedresulting in the phase modulated signal.

Considering BPSK as an example scenario, a complex synchronousdemodulator is used to recover data in noisy environments. FIG. 9 showsthe signal processing that one receiving channel is expected to performfor a BPSK signal in the form of a block diagram 900. Shown is anexcitation function module 901, a sensor 902, a Voltage ControlOscillator (VCO) 906, a 90 degree shift module 908, low pass filters904, 910, a step function module 920, an integrator module 930 and amaximum absolute value (MAX ABS) output 940.

The demodulation process can be divided into three major steps. Firstly,the signal undergoes a process called ‘carrier recovery’, in which aphased locked loop (e.g. a Costas loop) recovers the frequency and thephase of the modulated signal. In its classical implementation, a VCO906 adjusts the phase of the product detectors to be synchronous withthe carrier wave and a low pass filter (LPF) 904, 910 is applied to bothsides to suppress the upper harmonics.

The baseband signal obtained as the complex summation of the in-phase(I(t)) and the quadrature (Q(t)) components of the input carries theinformation of the phase ϕ between the reference/source signal and theinput signal. In fact.

$\begin{matrix}{\phi = {\tan^{- 1}\left( \frac{Q(t)}{I(t)} \right)}} & (14)\end{matrix}$ and: $\begin{matrix}{{\gamma(t)} = {{I(t)} + {i{Q(t)}}}} & (15)\end{matrix}$

The second stage consists in scanning the baseband signal with anappropriate matching filter, computing the absolute value of thefollowing product:

$\begin{matrix}{{r_{xy}(t)} = {{\sum\limits_{\tau = 0}^{N}{{x(t)} \cdot {y\left( {t - \tau} \right)}}}}} & (16)\end{matrix}$

where r_(xy)(t) is the cross-correlation, x(t) is the chosen matchingfilter, y(t) is the baseband signal, and the parameter T is any integer.For BPSK modulation, x(t consists of a −1 and a +1. The adoption of aBPSK scheme reduces the second stage to a simple multiplication for +1and −1, making the processing on chip computationally efficient.

In the third and last stage, the peaks of the cross-correlation signalare extracted, as they are proportional to the ToF. In fact, a maximumof the absolute value of cross-correlation corresponds to a perfectmatch between the complex demodulated signal and the matching filter,and hence to the instant of time at which the change in the phaseappears in the received signal.

B. Source Location Estimation

If only a limited number of receivers in the 3D space, and hence only alimited number of ToF estimations, is available, the target can betreated as a virtual source of reflected waves, allowing geometricaltechniques, such as triangulation, multilateration and methods based onhyperbolic source location estimation, to estimate the position of thevirtual source. They are introduced in the following section. Moreover,knowing the direction of arrival of wave-front in the far field, canhelp to constrain the location of the source even further. The problemof DoA estimation is important since it gives vital information aboutthe bearing of the source.

1. Trilateration

Trilateration is the process of determining absolute or relativelocations of points by measurement of distances, using the geometry ofcircles, spheres or triangles. Trilateration has practical applicationsin surveying and navigation (GPS) and does not include the measurementof angles.

In two-dimensional geometry, it is known that if a point lies on twocircles, then the circle centers and the two radii provide sufficientinformation to narrow the possible locations down to two.

In three-dimensional geometry, when it is known that a point lies on thesurfaces of three spheres, then the centers of the three spheres alongwith their radii provide sufficient information to narrow the possiblelocations down to no more than two (unless the centers lie on a straightline). Additional information may narrow the possibilities down to oneunique location. In haptic feedback systems, triangulation can be usedto get the coordinates (x, y, z) of the virtual source in air (or of itscenter of mass). Its position lies in the intersections of the surfacesof three (or more) spheres.

Consider the trilateration problem shown in FIG. 10, which shows aschematic 1000 where P1 1010, P2 1020 and P3 1030 are the position ofthree receiving transducers. The intersections 1040 of the surfaces ofthree spheres is found by formulating the equations for the three spheresurfaces and then solving the three equations for the three unknowns, x,y, and z. The formulation is such that one transducer's position is atthe origin of the reference system and one other is on the x-axis.

$\begin{matrix}\left\{ \begin{matrix}{r_{1}^{2} = {x^{2} + y^{2} + z^{2}}} \\{r_{2}^{2} = {\left( {x - d} \right)^{2} + y^{2} + z^{2}}} \\{r_{3}^{2} = {\left( {x - i} \right)^{2} + \left( {y - j} \right)^{2} + z^{2}}}\end{matrix} \right. & (17)\end{matrix}$

where d is the x coordinate of point P2 (receiver no. 2), i and j arethe x and y coordinates of the point P3 (receiver no. 3) with respect tothe chosen reference system, and r₁, r₂, r₃ are the time-of-flights atthe three receivers' positions.

It is necessary to find a point located at (x, y, z) that satisfies allthree equations.

The next step is to use r₁ and r₂ to eliminate y and z and solve for x,as follows:

$\begin{matrix}{r_{1}^{2} = {x^{2} + y^{2} + z^{2}}} & (18)\end{matrix}$ r₂² = (x − d)² + y² + z² r₁² − r₂² = x² − (x − d)²r₁² − r₂² = x² − (x² − 2xd + d²) r₁² − r₂² = 2xd − d²r₁² − r₂² + d² = 2xd $x = \frac{r_{1}^{2} - r_{2}^{2} + d_{2}}{2d}$

Substituting z²=r₁ ²−x²−y² into the formula for the third sphere andsolving for y, yields to:

$\begin{matrix}{y = \frac{r_{1}^{2} - r_{3}^{2} - x^{2} + \left( {x - i} \right)^{2} + j^{2}}{2j}} & (19)\end{matrix}$

Now that the x and y coordinates of the solution point are found; theformula can be rearranged for the first sphere to find the z coordinate:

z=r ₁ ² −x ² −y ²  (20)

2. Multilateration

Multilateration (MLAT) is a surveillance technique based on themeasurement of the difference in distance to two stations at knownlocations by broadcast signals at known time. Multilateration relies onmultiple measurements to estimate the location of an object. For thepurposes of this document, the objective consists of adjusting theparameters of a model function to best fit a data set. A suitable modelfunction has the following form:

f(r)=(x _(i) −x _(s))²+(y _(i) −y _(s))²+(z _(i) −z _(s))²−(T _(i)·c)²  (21)

where r=(x_(s), y_(s), z_(s)) is the vector of coordinates of theimaginary source and x_(i), y_(i), z_(i) are the coordinates of the i-threceiving transducer. The least squares method finds its optimum whenthe sum S of squared residual is a minimum:

$\begin{matrix}{S = {\sum\limits_{i = 1}^{n}{w_{i} \cdot \left( {f(r)} \right)^{2}}}} & (22)\end{matrix}$

where w_(i) is a weight assigned at each measurement for each ntransducer.

The vector gradient

${\nabla f} = \left( {\frac{df}{dx},\frac{df}{dy},\frac{df}{d_{Z}}} \right)$

is expressed as it follows:

$\begin{matrix}{{\nabla f} = \begin{bmatrix}{\sum\limits_{i = 1}^{n}{{- w_{i}} \cdot 4 \cdot {f(r)} \cdot \left( {x_{i} - x_{s}} \right)}} \\{\sum\limits_{i = 1}^{n}{{{- w_{i}} \cdot 4 \cdot f}{(r) \cdot \left( {y_{i} - y_{s}} \right)}}} \\{\sum\limits_{i = 1}^{n}{{{- w_{i}} \cdot 4 \cdot f}{(r) \cdot \left( {z_{i} - z_{s}} \right)}}}\end{bmatrix}} & (23)\end{matrix}$

Then, a loop of N iterations updates the parameters of the modelfunctions, in a gradient descent way, according to the followingexpression:

r*=r−ε·∇f  (24)

where r* is the updated vector of coordinates and ε is an arbitrarilychosen constant. The vector gradient is updated N times until it becomesreasonably small. The weights w_(i) are set to be proportional to thetime-of-flight, as the following expression:

w _(i)=(T _(i) −c)^(α)  (25)

where T_(i) is the time-of-flight at the i-th receiver, c is the speedof sound and α is arbitrarily chosen (usually varying between 1 and 2).

Another alternative, suitable model function has the following form:

$\begin{matrix}{S = {\sum\limits_{j}^{n}{\sum\limits_{i}^{n}\left( {{A\left\lbrack {i,j} \right\rbrack} - {{CC}\left\lbrack {i,j} \right\rbrack}} \right)^{2}}}} & (26)\end{matrix}$

where A is the matrix of the difference of time of arrival (DToA)between all the possible combination of sensor pairs, and CC is thegeneralized cross-correlation matrix of DToA.

3. Hyperbolic and Trigonometric Methods

The position of the virtual source, given the delay between two or moresensors, can be estimated finding the intersection of multiplehyperbolae. In fact, it can be assumed receivers' pairs to lay on thefoci of one hyperbola. Hence, the interception of multiple hyperbolae,each one corresponding to multiple receivers' pairs, lead to a crude,but computationally efficient, estimation of the source location, rangeand bearing.

C. Direction of Arrival Estimation

DoA estimators are classified in two main categories, namelyspectral-based (non-parametric) approaches and eigen-based (parametric)approaches. They are all based on the assumption of far-fieldconditions, i.e. the radius of propagation is so large (compared to thesize of the array of transducers) that the wave-front propagates as aflat plane of constant phase. They estimate the direction of arrival ofthe wave-front given a set of receivers usually arranged in a linear orcircular array geometry.

Spectral-based methods are based on the adoption of a model of thereceived narrowband, plane wave-front, and on the construction of aspectrum-like function of the parameter of interest (DoA). Conventionalbeamformer and the Capon's method are two examples of spectral-basedestimators. They are based on the idea of ‘steering’ the array ofreceivers in one direction at time, and to measure the output power. TheDoA estimates are associated with the steering locations with maximumpower. Eigen-based algorithms are based on an eigen decomposition and onthe extraction of a portion of the subspace. For example, the MUSICalgorithm only considers the eigenvectors associated to the smallesteigenvalues, exploiting their feature to be orthogonal to the steeringvectors.

Since DoA estimators are bound by the analogue of the Nyquist' samplingcriterion in space, the sensor spacing, d, should always be smaller thanhalf the wavelength of interest, λ, as follows:

$\begin{matrix}{\frac{d}{\lambda} < 0.5} & (27)\end{matrix}$

They can be applied to the amplitude/frequency/phase modulationsinusoids in such cases where the carrier's wavelength fails to fallwithin Nyquist's. DoA estimators can be used to further constrain thevirtual source position, estimated with geometrical methods and gradientdescent methods.

II. Optical Tracking System

A single optical camera would integrate and complement the objecttracking performed with acoustic methods, by constraining the twocoordinates of its projection plane.

In the case of haptic feedback systems, while the acoustic range findingand plane solving system constrains the plane of the hand in angles anddistance above the device, a single optical camera can be used tofurther constrain the hand detection in the spatially perpendiculardegrees of freedom. Ideally, this would then yield targetable locationsfor the haptics, while as a side effect the optical camera can provideinput to a gesture detection system.

As introduced in section I, camera tracking systems invite privacyconcerns and are bandwidth heavy because they transport video dataaround. An optical tracking system intended to be included in a cheapembedded system must endeavor to intelligently eliminate as much of thisexpensive bandwidth as quickly and simply as possible while retainingall the necessary tracking data. Such a system has a low latency and canrespond and track quickly, and importantly also has a low build cost.

As the detection of depth and plane angles have been effectively solvedprior to this by the ultrasonic tracking system, a singleoptical/electromagnetic camera can be used to achieve further spatialconstraints to fix the hand position in three dimensions. Such a camerasystem has previously demonstrated to be low cost, but not so with theassociated processing and computer vision, as these existing systemsshuttle the relatively large video data to be processed externally.

Bringing together the two concepts of minimizing the bandwidthcross-section of the exported data and bringing the initial processingon-chip leads to considering a series of computer vision algorithms towhittle down later processing requirements. Further, this must alsomaintain the fidelity derived from the initial video data to finallyenable accurate haptic feedback to be projected onto the hand. Toachieve this, a pipeline is effectively created that transformsgreyscale video images into a skeleton: a representation of theinteractive part of the scene as a hierarchical set of medial lines.

Alternatively, more than one camera can be used at the same time toobtain the coordinates of the tracked object in the tri-dimensionalspace with conventional methods (time-of-flight, stereo vision orinterferometry), while continuously refining and calibrating the rangewith ultrasonic measurements.

A. Computer Vision Video Pipeline

The pipeline of data for recognizing a hand and achieving trackinginvolves taking an image as input and outputting the location andtopology of the hand. To achieve this, algorithms are applied to producea topological skeleton while bearing in mind that the bandwidth andcompute is to be minimized. An algorithm that eschews floating-pointarithmetic would also be much more amenable to hardware implementation,so given this the following pipeline seems to achieve our given aims.

1. Reduction to a Binary Image

The first step of the image processing is to take the raw input‘greyscale’ image from the camera sensor to a binary silhouette of theobjects to be detected. This is generally achieved through standardbackground subtraction algorithms, the most effective of which is themixed Gaussian model, wherein for each pixel a mixture of Gaussiansdescribing the distribution of pixel values is maintained. TheGaussian(s) with the most power represent the background of the image,while any pixel falling outside of a given sigma range are labelledforeground. A median nearest-neighbor filter is used to reject spuriousmisclassifications and the background and foreground are then used toconstruct the binary image.

2. Meijster Distance Transform Squared

From the binary image, the distance transform is computed. This can bedescribed as replacing the pixel values at each location in theforeground with a value representing the shortest distance to anybackground pixel. To begin with, the work by Meijster et al. (A.Meijster, J. B. Roerdink, and W. H. Hesselink, “A general algorithm forcomputing distance transforms in linear time,” in MathematicalMorphology and its applications to image and signal processing:Springer, 2002, pp. 331-340) is considered. This method is then alteredby considering a formulation that both omits the expensive final squareroot and as a result allows the entire squared distance field to beexpressed using exact integer arithmetic. This removes error introducedby rounding and allows this algorithm to use the minimum possible amountof arithmetic. The Meijster algorithm involves one parallel 1D distancepass followed by a second pass using a parallel stack machine on eachcolumn. At the same time, the point with the largest square distance isrecorded as the root node of the skeleton.

3. Stationary Point Detection

On the square distance transformed binary image, a stationary pointdetection is applied (at first, Laplacian type edge detection wasposited instead but it was ultimately not sufficiently robust). Thiseffectively is a pass that highlights only points which are localmaxima. This is achieved by picking out foreground points for whompoints to the left are strictly smaller squared distances and points tothe right are smaller or equal squared distances along both vertical andhorizontal directions. The small number of points for whom this is trueare added onto a specialized priority queue for further processing.

4. Point Queue

When points are added onto the priority queue, the square root ofdistance transform and so the final step of the Meijster distancetransform is taken. A novel warped distance measure is then obtained bycomputing the real spatial distance from the root of the hierarchy andsubtracting from it the Meijster distance value. This warped distancemeasure is then the priority of the node placed in the priority queuewith the coordinate pair of this point attached.

5. Modified Prim's Algorithm—Constructing a Warped Minimum Spanning Tree

The final minimum spanning tree is found by keeping a list of boundarynodes and a list of already connected nodes. To begin with the root nodeis on both lists. As each node is de-queued it is connected to anexisting node that minimizes the warped distance measure, which is againthe spatial distance between the de-queued node and the existing node,with the difference in the Meijster distance transform value between thede-queued node and the existing node subtracted. This step is intendedto minimize circuitousness in the links between the chosen nodes. Eachnewly attached node is added to the boundary node list which is searchedfor small distance points that could be added cheaply first, resultingin a replacement of the matched node in the boundary node list with thede-queued node on the event of a successful match. As the warpeddistance measure is necessarily positive (which means that moreexpensive minimum spanning tree algorithms which are compatible withnegative arc weights need not be considered), a full iteration of Prim'sminimum spanning tree algorithm can be engaged when the boundary pointlist fails to find an obvious choice of tree extension, resulting in anew node on the boundary node list. This must occur at least on everytree bifurcation.

6. Gesture Detection

Gesture detection can now be achieved by taking this set of pointsarranged into a minimum spanning tree (which will be termed a medialline model) and using machine learning or other classification systemsto attach semantic meaning to the spatial node hierarchy. As the rootnode is always the first in the list of points and relationships given,the center of mass of the hand is known, so the haptic feedback can beprojected effectively in a way that is directed in the other dimensionsthat are not as well determined by the ultrasonic range finding basedtracking system.

B. Trial Run of Video Rate Detection

Video rate hand tracking using the algorithms developed above involvedtaking a camera, infrared bandpass filter, infrared LED illuminationsource and piping the data through a video subsystem on a PC. Capturingand detecting the hand to verify the pipeline in a real-time videosetting was achieved and verified by overlaying the detected hand datawith the video input in real-time. Background subtraction was achievedwith a simple, but unreliable heuristic that this test showed could useimprovement, which is why in this document it is replaced with themixture of Gaussians technique.

FIG. 11 shows a binary image 1100 depicting a hand wherein white is theforeground and black the background.

FIG. 12 shows the binary image 1200 after the square Meijster distancetransform has been applied. Multiples of 256 create the bands ofgreyscale shown here, highlighting the quadratic, non-linear nature ofthe distance measure.

FIG. 13 shows the image 1300 after the detection of the stationary localmaxima points, shown here in white.

FIG. 14 shows the final skeletonized result 1400 from the image in FIG.13, with only the medial lines of the region in white.

Finally, FIG. 15 shows a single camera infrared hand tracking 1500 to beviable in a real-time setting.

C. Evaluation of Hand Detection

The method as presented above also has some drawbacks. To be able todetect multiple objects, some segmentation of the original binary imagemust be applied to detect separate nodes on separate trees that haveseparate root nodes. Otherwise, the method will try to combine multiplehands into one hierarchy, regardless of whether they are physicallyconnected.

Simple heuristics are useful here, for instance when segmenting thebinary image to work on each detected object separately it is useful totake the largest in area n groups of pixels belonging to detectedobjects and only perform work on these. This helps to ensure that thehardware has sufficient time to compute the necessary medial line modelhierarchies for each segmented object group. It may also be of interestto use simple thresholding in combination with median filtering toobtain the initial binary silhouette, whose smoothness is key due to theuse of the.

There are also improvements to be had in the construction of the minimumspanning tree wherein nodes that are repeated and do not add significantextra data can be dropped to remove complexity and save bandwidth whenstoring the minimum spanning tree. This can be achieved by usingEuclidean distance or reusing the warped distance metric—when thedifference in straight line distance is close enough to the sum of thedistances to nodes at increasingly large distances away, thenintervening nodes may be dropped. This could help to keep the spanningtree down to a fixed size for the purposes of ensuring that memory andbandwidth limits are respected. This step may also be necessary toconvert the spanning tree constructed of medial lines into a skeletalmodel.

It is also not clear to what extent the mixture of Gaussians techniqueis necessary for background subtraction and segmentation as due to theircomplexity and storage requirements it would be helpful to avoid using aper-pixel statistical model to segment the background. However, theinitial production of a high quality binary image is of paramountimportance to the algorithm, and so if no other effective backgroundsubtraction algorithm can be found, the mixture of Gaussians seems to bethe gold standard approach although potentially complicated with a highlevel of resource usage from the standpoint of an embedded approach.

III. Sensor Fusion and Applications

With the integration of multiple data coming from the two differentprinciples (acoustic and optical) in a cheap embedded system, it ispossible to achieve the complete tracking of an object floating inmid-air. The expenses required to process the acoustic and optical datais intended to be very low and to happen on-chip, in order tointelligently eliminate as much of the expensive bandwidth (that commonToF cameras share) as quickly and simply as possible, while retainingall the necessary tracking data. This is so that such a system has a lowlatency and can respond and track quickly, but importantly also have alow build cost.

Gesture applications aim at remotely control home appliances, automotivedashboards, smart televisions or portable devices, by translating humanmovements into actions and directives. Since fast response time, lowlatency, medium range, low power consumption and centimeter-levelaccuracy are usually a requirement, the sensor fusion system isnaturally targeted for these applications. Gesture detection can beachieved by taking different features corresponding to relevant featuresof the tracked target and using machine learning or other classificationsystems to attach semantic meaning.

FIG. 16 depicts the flowchart 1600 of the sensor fusion systemsdescribed herein. Location data 1670, 1680 is fed into a start module1610, when then associates with an ultrasound 1620 and 2 single EMcameras 1630, 1640. The ultrasound 1620 then associates with az-coordinate bearing 1650 via range measurement with ToF techniques. Thefirst single EM camera 1630 associates with an x-coordinate,y-coordinate bearing 1655 and/or an x-coordinate, y-coordinate,z-coordinate bearing 1660. The second single EM camera 1640 associateswith the x-coordinate, y-coordinate, z-coordinate bearing 1660. The EMcameras 1630, 1640 may use stereo-vision, ToF or interferometry.

A first fusion module 1665 gathers data from the z-coordinate bearing1650 and the x-coordinate, y-coordinate bearing 1655 to calculate afirst location 1670. A second fusion module 1668 gathers data from thez-coordinate bearing 1650 and the x-coordinate, y-coordinate,z-coordinate bearing 1660 to calculate a second location 1680. Both ofthese processes refine measurement of range using ultrasound as groundtruth.

IV. Additional Disclosure

1. A time-of-flight sensor fusion system for depth and range sensing ofobjects which integrates multiple data coming from embedded acoustic andone optical camera.

A system of paragraph 1, which uses amplitude, frequency, phasemodulation or a combination of those, to modulate the emitted acousticsignal

A system of paragraph 1, which uses a combination of temporal andspatio-temporal acoustic modulation techniques

A system of paragraph 1, which uses narrowband signal as an emittedacoustic signal

A system of paragraph 1, which uses a broadband signal as an emittedacoustic signal

A system of paragraph 1, which uses virtual acoustic point sources as amethod to perform tracking while producing a haptic feedback

A system of paragraph 1, in which a pipeline of EM/infrared data is ableto recognize a hand and output the location and topology of the hand inits projection plane

A system of paragraph 1, which uses trilateration based on time ofarrival, to estimate the position of the center of mass of the trackingobject with respect to the array of acoustic sensors

A system of paragraph 1, which uses multilateration based on time ofarrival or on difference of time of arrival (DToA), to estimate theposition of the center of mass of the tracking object with respect tothe array of acoustic sensors

A system of paragraph 1, which uses hyperbolic and trigonometric methodsbased on time of arrival, to estimate the position of the center of massof the tracking object with respect to the array of acoustic sensors

A system of paragraph 1, which uses one or more methods to estimate thedirection of arrival, to further constrain the position of the target

2. A time-of-flight sensor fusion system for depth and range sensing ofobjects which integrates multiple data coming from embedded acoustic andmultiple optical camera.

A system of paragraph 2, which uses amplitude, frequency, phasemodulation or a combination of those, to modulate the emitted acousticsignal

A system of paragraph 2, which uses a combination of temporal andspatio-temporal acoustic modulation techniques

A system of paragraph 2, which uses narrowband signal as an emittedacoustic signal

A system of paragraph 2, which uses a broadband signal as an emittedacoustic signal

A system of paragraph 2, which uses virtual acoustic point sources as amethod to perform tracking while producing a haptic feedback

A system of paragraph 2, in which a pipeline of EM/infrared data is ableto recognize a hand and output the location and topology of the hand inits projection plane

A system of paragraph 2, which uses trilateration based on time ofarrival, to estimate the position of the center of mass of the trackingobject with respect to the array of acoustic sensors

A system of paragraph 2, which uses multilateration based on time ofarrival or on difference of time of arrival (DToA), to estimate theposition of the center of mass of the tracking object with respect tothe array of acoustic sensors

A system of paragraph 2, which uses hyperbolic and trigonometric methodsbased on time of arrival, to estimate the position of the center of massof the tracking object with respect to the array of acoustic sensors

A system of paragraph 2, which uses one or more methods to estimate thedirection of arrival, to further constrain the position of the target

3. A single camera optical system for detecting object pose wherein;

the input camera image is reduced to a binary image wherein each pixeleither does or does not belong to a detected object;a squared signed distance transform is performed on each pixel to detectthe square of the two-dimensional Euclidean distance to the objectboundary.

A system of paragraph 3, wherein stationary points are detected and usedto build a medial line model.

A system of paragraph 3, wherein the root node of the medial line modelis chosen to be the node with maximum squared distance from theboundary.

A system of paragraph 3, wherein stationary points making up potentialnodes for a medial line model may be culled using a squared spatialdistance metric.

A system of paragraph 3, wherein the square root of the squared distancefrom the boundary is computed only for potential nodes of a medial linemodel.

A system of paragraph 3, wherein a warped distance metric is computedfor each potential node in a medial line model which is the spatialdistance to the root node with the distance to the boundary subtractedfrom it.

A system of paragraph 3, wherein the edges of the medial line model areconstructed by applying a hybrid of a greedy algorithm and a classicalminimum spanning tree algorithm to a priority queue of potential nodes.

(5). Phase Modulated Spherical Wave-Fronts in Acoustic Phased-Arrays

1. Phase Modulated Spherical Wave-Fronts in Acoustic Phased-Arrays

As previously disclosed, one way of tracking a user's hand is by meansof an optical camera. Introducing a phase modulation of the sinusoidalcontinuous waves enables the tracking of an object in mid-air bytime-of-flight estimations.

In the following example, a message is encoded in the sinusoidaltransmitted signal in the form of many sharp flips of phase at knowninstants in time. A received signal is recorded and demodulated at someremote locations by means of receiving transducers. Introducing a phasemodulation into the transducers' signal allows receiving transducers ormicrophones to synchronize on the transmitted signal, yielding theability to detect the distance of an object, such as the hand, from thearray.

Inserting phase flips in the in-phase carrier frequency of eachtransducers of a 2D array, in such a manner to make them collide at afocus, yields the generation of spherical wave-fronts with differentphases, within, for example, an in-phase wave-front. The tracking systemdescribed herein exploits the benefit of having a spherical spreadingwave-front, as opposed to focusing techniques. In fact, thespherical-spreading feature increases the spatial resolution of theacoustic tracking system by spreading the acoustic power over biggervolumes, especially if compared to the beamforming techniques mentionedabove. The tracking waveform should be a signal made up of suitablyorthogonal functions so that it may be picked at receivers' locations.They could be a known sequence of encoded phase shifts. These signalswould be reflected from objects in the field allowing existing echoprocessing techniques, such as multilateration, to perform tracking.

The concept of spherical phased modulated wave-front is antithetical tobeamforming. In fact, the wave-front can be generated assuming a focuslocated at negative heights with respect to the position of the phasedarray, which can be called anti-focus. It is effectively the center ofthe generated sphere. In this case, acoustic waves combine to produce aspherical wave that appears to come from behind the emitters. Theposition of the anti-focus effectively determines the radius and thesteering of the sphere, and hence its ability to spread over smaller orbigger volumes of the medium.

FIG. 17 is a set of graphs 1700 showing the signal emitted by the first5 transducers of a 1D array of 16 transducers in order to produce aphase modulated wave-front with phase equal to n, with an anti-focus atnegative 0.16 m at a central position, a carrier frequency of 40 kHz andan in-phase amplitude modulated wave-front. Each signal graph 1710,1720, 1730, 1740, 1750 has an x-axis 1760 of time in seconds and ay-axis1770 of normalized amplitude. FIG. 17 shows that the carrier wave foreach individual transducer is in-phase, whilst the phase discontinuityis introduced at different instances in time.

FIGS. 18A and 18B show two snapshots 1800, 1850 from the video of anumerical simulation, showing the acoustic pressure wavefield in thecase of an anti-focus at negative 0.16 m above the array and centrallylocated, with a flat horizontal obstacle placed at 0.2 m. FIG. 18A is asimulation 1800 with an x-axis of y-position in mm 1810 and a y-axis ofx-position in mm 1820 with a snapshot 1830. FIG. 15B is a simulation1850 with an x-axis of y-position in mm 1860 and ay-axis of x-positionin mm 1870 with a snapshot 1880.

In the simulation, the transducers are considered to be omnidirectionalpoint sources, and equally spaced by 0.01 m. The spherical phasewave-front generated from the array is visible at a height ofapproximately −50 mm in both plots. The spherical phase wave-frontgenerated is moving upwards, toward the reflector in FIG. 18A, whilemoving back after being reflected off the surfaces of the obstacle inFIG. 18B. The black dotted lines show the positions.

II. Tracking of the Object

As previously discussed, the introduction of phase and/or frequencymodulation into the transducers' signal yields the ability to detect thedistance of an object, such as the hand, from the array and controlpoint. Each receiver yields an estimation of the distance of the object.In case phase modulation is adopted, the signal that arrives at thereceiving location is a complicated analog waveform that needs to bedemodulated in order to recover the original message. The demodulationis accomplished through a standard process called ‘carrier recovery’which consists of figuring out both the frequency and phase of themodulating sinusoid, or by locking the phase with respect to a referenceclock, when possible.

The phase modulation and the frequency at which phase inversions areencoded in the signals can be dynamically tailored to match the sensingobjective and the environment.

The presence, location and distance of the reflector in space isrevealed once the time-of-flight is recovered. Moreover, if thereflector does not have a predominant dimension, atrilateration/multilateration process would reveal its approximateposition in the tri-dimensional space. At contrary, if the reflector hasa predominant dimension, it could be possible to trilaterate theequation of the plane of best approximation relative to an arbitrarycoordinate reference system in the tri-dimensional space.

III. Additional Disclosure

1. An acoustic technique in which phase modulation is used to generatespherical phase wave-fronts within a multitude of different amplitudemodulated wave-fronts.

A method of paragraph 1, which is used for object tracking.

A method of paragraph 1, which is used for haptics and object trackingsimultaneously.

A method of paragraph 1, which is interpolated with some focused statesto create focused regions of acoustic power (for example to producehaptic sensation) and track objects simultaneously.

A method of paragraph 1, in which the modulation parameters can bedynamically tailored to match the sensing objective and the environment.

(6). Long Wavelength Phase Modulation of Acoustic Field for Location andTracking of an Object

I. Introduction

The description relates to the algorithm, data path architecture, IP andimplementation of a technique by which the location of an object withinan acoustic field may be determined. More specifically, the field has acharacteristic of a phase modulation added to the otherwise naturalphase of the acoustic field. Furthermore, the modulation is a longwavelength sinusoidal modulation in a specific example, but not limitedto such an example.

The description applies specifically to location in a volume of air inthe presence, or using, an acoustic field. The approach is the basis ofa large number of more sophisticated algorithms, approaches andcapabilities that would effectively use the fundamental distance andlocation information provided by the base layers of the process. Forexample, triangulation to locate the reflector within the volume, ormultilateration techniques to apply more sophisticated and more capableprocessing to the base data in order to extract higher levels ofconclusions.

The use of a long modulation wavelength allows one to distinguish awider range of physical displacements without ambiguity in the signal'sphase, and also improves the forgiveness of the overall system to thesmall perturbations that would be considered irrelevant, or even noise,in the system. The use of a slowly changing sinusoidal modulationreduces the undesirable effects of audible noise that may result fromtransient approaches with the transducer arrays.

Spatial Nyquist requirements mean that sensors need to be separated byless than half the wavelength of the field to be sensed. For shortwavelength fields, this would mean a high density of small sensors wouldbe required. The use of a long wavelength further allows sensors to beplaced relatively far apart, for example to span the dimensions of atransducer array, while still maintaining the spatial Nyquistrequirements. This reduces the costs of implementation and makes itpossible to build various configurations of array that includetransducers for emission and sensors arranged optimally with reasonablesized sensors and transducers.

The long wavelength phase modulation technique may be used concurrentlywith a haptics field without the need for a specific time slot in whichto stop the haptics and generate a field specifically for the tracking.In order to recover the phase modulation, knowledge of the haptics phaseis required, and this could be made available by the solver whichdetermines the transducers' relative phase trajectories in the firstplace. Alternatively, rather than seeking knowledge of the phases of theemitters in order to remove from the sensed field, it is conceivablethat the phases of the emitters are changing sufficiently rapidlyrelative to the long wavelength modulation that they may be simplyfiltered off as part of the phase demodulation. Noting that the longwavelength modulation may be configured with different wavelengths, thenit is further possible to use a wavelength which is known to besufficiently different from the emission phases such that recovery ofthe wanted signal is facilitated, and to make this choice for eachvariant of the emitted field.

This technique may use continuous modulation, and hence continuoussensing of location. This enables a high update rate for the calculatedlocation of the object, not limited by physical fundamentals such as thespeed of sound or the range of the location and tracking. The updaterate would be limited only by the rate at which the electronics is ableto process the relevant data, which then gives the product designerflexibility to tradeoff between cost and performance. This then yieldsan enhanced tracking capability through higher resolution of thelocation and also smoother tracking trajectories. Tracking an object ina volume as the object moves is made possible and enhanced. Gesturerecognition using simple signal processing becomes possible with thereal-time tracking of reflectors.

The key parameters of the modulation may be modified, or manipulated inreal time, to enable further capabilities.

One example is to vary the modulation index (for SNR in the demodulatedphase, for example), or the modulation wavelength depending onrequirements, or array geometries and distance involved for example whenthe reflector is moving.

Another example is to partition the array spatially, for example intoquadrants, and apply a phase modulation of different wavelengths to theemitters in each quadrant. This way the sensed signals' phasemodulations may be used to determine not only the location of thereflector, but also the orientation of the reflector from the relativepowers of the different wavelengths sensed.

This approach is scalable to higher levels of sophistications, tovarieties of sensors, be they transducers of omnidirectional MEMSmicrophones, and to geometries/applications.

It is conceivable that the emitters and sensors be at differentlocations around a volume, for example around the cabin of a vehicle, aslong as their relative positions are known or can be discovered by thesystem.

II. Phase Modulation of Fields

A phase is considered modulated if the phase, or timing, of the signalis altered in a manner that is known or may be determined. If the phasemodulation conforms to certain bounding criteria, then the phasemodulation may be considered to be a form of coding of information intothe signal. Recovering the phase modulation allows one to effectivelydecode the information and determine key features that the coding wasintended to convey, for example the time of flight.

The phase modulation may be the only active component in the field, forexample in an otherwise planar acoustic field with no specific phaseactivity. In this scenario the plane wave is assumed to have alltransducers in an array at the same carrier phase as each other. Thephase modulation is added to the natural phase of all of thetransducers, and thus the whole field's phase is modulated relative tothe carrier.

Additionally, the phase modulation may be added to the otherwise naturalphase of any acoustic field. For example, a field generated to steer orfocus energy within a volume requires that the relative phases of thetransducers be specified, coordinated and controlled. Therefore, thenatural phase of such a field is unlikely to be planar and may also bechanging with time. In this scenario, the phase modulation is added in asynchronized fashion to all transducers, and thus create a modulatedphase baseline upon which the steering and/haptic field may be built.This means that this approach can be used concurrently with haptics.

III. Range of Wave Shapes

It is conceivable, and advantageous in certain circumstances, togenerate acoustic fields with different characteristics. For example, aconvex or concave phase wave front where the phase modulation eitherspreads out in the volume or becomes focused in the volume respectively.

Further alternatives would include a scanning focal point of phasewithin the volume, or a scanning wall of modulated phase. These latteroptions offer the opportunities to also locate the boundaries of thereflector.

IV. Sensing Reflections and Location

A generated field reflects off objects within the volume of the field.The reflected waves are sensed, generating electrical signalsrepresenting the waves sensed by the sensors, and the signals may bedigitized for further digital processing. The phase modulation recoveredfrom the sensed signals and is compared to the reference phase todetermine the distance travelled by the wave. A distance may becalculated for each sensor, and thus knowing the arrangement of sensorsallows one to calculate the location of the reflector.

With a sufficient number of sensors (e.g. three or more) it is possibleto determine the 3D location when the sensors are all located in aplane. More sensors yield improved precision and accuracy.

Sensors are used to sense the pressure waves in the volume and togenerate electrical signals that represent the sensed pressure levels.The phase modulation of a reflected wave is recovered from theelectrical signal and this recovered phase is compared to the referencephase to determine the effective time of flight of the sensed wave, orequivalently the distance from emitter, to reflector, then to sensor.The reference modulation phase is known to the system, either a priorior else through sensing the modulated emissions directly. It requiresknowledge of the unmodulated carrier, the modulated carrier and thesensor signals. These can all be sampled from an operational array. Theoverhead of the carriers' waveforms is shared among all the sensors.

FIG. 19 shows a data path schematic 1900 that can be used to determinethe distance of a reflector from one sensor. A reference carrier 1905interfaces with a reference modulated carrier 1950 and then with a firstlow-pass filter 1920 before being processed by a first arctangent module1930. The reference carrier 1905 is also processed by a firstdifferentiator 1910 and then: (i) interfaces with a reference modulatedcarrier 1950 and then with a second low-pass filter 1925 before beingprocessed by the first arctangent module 1930; and (ii) interfaces withthe receiver waveform 1935 before being processed by a third and fourthlow-pass filters 1950, 1940 before being processed by a secondarctangent module 1945. The results of the foregoing are: (i) fed into afifth low pass filter 1960 and then processed by a third arctangentmodule 1970; and (ii) processed by a second differentiator 1955 and thenfed into a sixth low pass filter 19605 and then processed by a thirdarctangent module 1970. The output of the third arctangent module 1970is fed to produce the temporal signal beta (P) 1975.

Two banks of low pass filters are used. The filters with label “LPF Wm”1920, 1925, 1950, 1940 indicate filters with corner frequency to allowthe modulation frequency to pass through largely unaffected, andattenuating components outside that band. The filters with label “LPFDC” 1960, 1965 indicate filters with corner frequency to allow only thevery low frequencies through largely unaffected, and attenuatingcomponents at other frequencies.

The blocks with labels “d/dt” 1910, 1955 indicate a differentiation, orsubtraction of adjacent sample values. An alternative approach forderiving the quadrature component of the reference modulation would beto apply a delay in the signal path at that node, equivalent to shiftingthe waveform by one quarter of a cycle.

FIG. 20 is a graph 2000 having an x-axis 2010 of normalized frequencyand a y-axis 2020 of dB showing the magnitude spectra of two signals:(i) the digitized signal representing the digital drive signal of thecarrier 2030; and (ii) the digital signal derived from the drive signalto represent the fundamental frequency of the carrier 2040. This isperformed by filtering out the undesirable harmonics and components inthe first signal. In other words, FIG. 20 shows the frequency magnitudespectrum of the transmitter drive signal, and the frequency magnitudespectrum of the reference carrier recovered from the drive signal.

A similar approach may be used to extract the modulated carrier from anequivalent digitized signal driving a phase modulated transmitter, forexample.

The reference modulation is extracted from the reference modulatedcarrier by mixing with the reference carrier. The following plots showthe phase modulation in the recovered reference carrier, that is theinline (I) and quadrature (Q) components of the reference phasemodulation.

FIG. 21 is a graph 2100 with an x-axis 2110 of time in seconds, a y-axis2120 of output in radians, a plot for I 2130 and a plot for Q 2140.

FIG. 22 is a graph 2200 showing reference phase modulation in the IQdomain.

The x-axis 2210 is I in radians, the y-axis 2220 is Q in radians and theplot 2230 shows the parametric plot between the two.

FIG. 23 is a graph 2300 showing inline/quadrature components andreference phase modulation. The graph 2300 has an x-axis 2310 inseconds, a y-axis 2320 in radians, an I plot 2330, a Q plot 2340 and areference modulation plot 2350.

FIG. 21 and FIG. 22 show the components of reference modulation, or themodulation of the phase of the transmitter signal. FIG. 23 shows the IQcomponents and the resulting extracted reference phase modulation. Thisis the reference against which the phase modulation of the sensed signalis compared in order to determine the total time of flight.

A similar mixing technique is used to recover the phase modulation froma sensed, or received, signal. The following shows the modulation of areflected wave recovered from a sensor.

FIG. 24 is a graph 2400 showing the Inline and Quadrature components ofthe received phase modulation. The graph 2400 has an x-axis 2410 oftime, ay-axis 2420 of radians, a plot for I 2430 and a plot for Q 2440.

FIG. 25 is a graph 2500 showing received phase modulation in the IQdomain. The x-axis 2510 is I in radians, the y-axis 2520 is Q in radiansand the plot 2530 shows a parametric plot between the two.

FIG. 26 is a graph 2600 showing Inline/Quadrature components andreceived phase modulation. The graph 2600 has an x-axis 2610 of time, ay-axis 2620 in radians, a plot for I 2630, a plot for Q 2640 and a plotfor received phase modulation 2650.

FIG. 24, FIG. 25 and FIG. 26 show the IQ components resulting from themixing, and the phase modulation in the received waveform recovered inthis manner.

The difference between the received phase modulation and the referencephase modulation is measured as shown in FIG. 19 by mixing the twosignals and combining to yield a temporal signal, β, representing theestimated difference between received and reference phase. FIG. 27 is agraph 2700 showing the time domain signals resulting from the estimationof the difference between the modulated phase in the receiver comparedto the reference modulated phase. The graph 2700 has an x-axis 2710 inseconds, a y-axis of radians 2720, a plot for the β I component 2730, aplot for the β Q component 2740 and a plot for the β time vector 2750.

FIG. 28 is a graph 2800 of the IQ domain view of estimated differencebetween received and reference modulated phase. The x-axis 2810 is inradians showing I, the y-axis 2820 is in radians showing Q and the plot2830 shows the parametric plot between the two. FIG. 28 shows the IQcomponents of the estimate in the IQ domain, yielding a smalldistribution of values and thus correctly indicating that the reflectoris stationary relative to the transmitter and receiver.

The estimate of phase difference is linearly related to the distancetravelled by the acoustic wave from transmitter, to reflector andfinally to the receiver. The time domain estimate of phase differencemay be filtered or averaged to yield a stable estimate of phasedifference, from which the distance travelled by the acoustic wave maybe calculated.

The above description is extended to include a number of transmitters,or a number of receivers, or multiple transmitters and receivers.

From the estimated distances, and geometry of the emitters and sensors,the location of the reflector may be determined. A number of approachesare possible here, starting with simple trigonometry and geometry. Otherapproaches are also possible, for example envelope of spheres centeredon each sensor or multilateration.

Orthogonal axes of sensors in a plane may be used to determine thelocation of the reflector projected onto the plane. Orthogonal axes inthree dimensions may be used to locate within a volume.

The axes do not need to be orthogonal to each other, but orthogonal axesprovide the highest precision for the space bounded by the sensors, orsize of an array.

The reflector's position, projected onto each axis, may be calculatedfor each axis joining sensors from knowledge of the reflector distanceto each of the sensors. Projecting on to two or more axes yields alocation in 2D or 3D. For a planar array of sensors, the axes would bein one plane and so these projections would yield a 2D locationprojected onto the array.

From two or more sensors, the angle of arrival may be estimated alongeach axis describing these sensors. Numerous axes may be utilized tolocate within a volume.

Furthermore, the elevation of the reflector is also estimated fromknowledge of the location of the sensors and the relative distances tothe various sensors. Combining the elevation with the location projectedon to the 2D plane yields the 3D location of the reflector.

A technique for recovering the modulation of the phase baseline from areflection in an active haptics acoustic field allows one to compare tothe reference modulation phase, and thus calculate the distancetravelled from emitter to sensor. The solver which determines therelative phases of the emitters has the required information, and ifthis is static then the task is simpler. However, even in a changinghaptic field it is conceivable that the phase information for thehaptics could be derived and removed from the net phase to arrive at thewanted phase modulation. Alternatively, if the wanted phase modulationwavelength is long compared to the wavelength (reciprocal rate of changeof phase) of the changing haptic field, then there is a strongpossibility of the mixing and filtering may remove the phase for thehaptics generation, yielding sufficient information indicating the phasemodulation. Furthermore, given that the wavelength of the phasemodulation may be altered, the choice of phase modulation wavelengthcould be made in order to facilitate the recovery of the modulated phasein the presence of the phase changing due to haptics since thecharacteristics of the haptic field are known a priori. These conceptsare yet to be demonstrated in a model but are certainly conceptuallypossible.

A further variant of the current solution is to compare the phasemodulation in two or more sensed signals directly and without the use ofknowledge of the transmitted signal. Direct comparison of modulatedphase between two sensed signals yields information regarding thedifferential phase between the two received signals, which in turn isproportional to the differential distance, or the difference in thedistance, travelled by the acoustic wave from transmitter to reflectorand then to each of the sensors. The differential distance may then beused to locate the reflector in the volume as well, for example derivingthe estimated angle of arrival of the wave. The advantage of thistechnique of comparing modulated phase of two received signals directlyreduces the complexity of the system, and therefore also the cost, byeliminating the requirement to provide a sampled stream of the referencemodulated carrier and also the processing associated with the samesignal. For full 3D location of the reflector in this scenario, theelevation of the reflector may need to complement the information gainedfrom the differential phase.

V. Additional Disclosure

Inventive steps of the foregoing may include.

-   -   Using a phase signal that is orthogonal to the haptics        generation, and so does not disrupt or degrade the haptics        generation.    -   Application of a long wavelength phase modulation of an acoustic        field as a signal generator for the purposes of sensing        reflected waves, determining the phase of the modulation in the        sensed signal through demodulation and comparing the demodulated        phase to the transmitting phase in order to determine the        distance of the reflector from the transmitter and receiver(s).    -   Use of a long wavelength, longer than the total distance        travelled by the wave, to remove ambiguity in phase between        transmitter and receiver that would result from using        wavelengths shorter than the total distance travelled by the        acoustic wave.    -   Continuous phase modulation of an acoustic field in real time        means that the sensing system may estimate distance continuously        and in real time (quantized only by the sampling rate of the        system), and so freeing the system update rate from the        constraints of the speed of sound that would apply in a sound        type of approach.    -   Long wavelength modulation may be configured with varying        modulation index and modulation wavelength in space and time to        better suit the particular application, range or environment.        Larger modulation index yields improved demodulated SNR and        signal dynamic range but spreads the energy of the modulated        carrier wider in the frequency domain and thus degrades raw SNR.        The longer modulation wavelengths allow measurement of distance        over a longer range without ambiguity, but the very long        wavelengths would require higher resolving power in the data        path, e.g. to resolve smaller rates of change of phase in the        presence of real system noise.    -   Use of configurable modulation wavelength allows one to use        spatial coding, for example an array may be partitioned into a        number of sub-sections, each of which using a different        modulation wavelength. The sensing system is then able to deduce        more information about the reflector, for example the distance        and orientation, by comparing the relative contributions of the        different modulation wavelengths present in each receiver's        signal.    -   The relatively low barrier to implementation, in terms of number        of sensors, compatibility with arrays, and current software,        makes this a key candidate for location/tracking IP.

Additional paragraphs of disclosure are:

1. A method comprising.

generating an acoustic field with known phase modulation;sensing acoustic energy reflected from an object;converting the acoustic energy into electrical signals;processing the electrical signals to determine location of the object inthe volume.

2. The method as in paragraph 1, wherein the phase modulation wavelengthis long compared to the carrier wavelength.

3. The method as in paragraph 2, wherein the wavelength is selected toeliminate spatial aliasing in the system while also allowing a sparsepopulation of receivers.

4. The method as in paragraph 2, wherein the rate of change of phase iscontrolled to reduce and eliminate audible side effects in the system.

5. The method as in paragraph 2, wherein the processing allows acontinuous streaming of data and update to the estimated location.

6. The method as in paragraph 2, wherein the phase modulation wavelengthvanes according to the location of the emitters in the system to applyspatial coding, and phase modulation wavelength may also vary in time.

7. The method as in paragraph 1, wherein the electrical signals aredigitized for subsequent processing.

8. A method as in paragraph 1, further comprising a phase modulationthat is orthogonal to other features of the acoustic which may be usedfor other purposes.

9. The method in paragraph 8, wherein the other purpose of the acousticfield is haptic feedback in midair.

10. The method as in paragraph 9, wherein the phase modulationwavelength is long compared to the carrier wavelength.

11. The method as in paragraph 10, wherein the wavelength is selected toeliminate spatial aliasing in the system.

12. The method as in paragraph 10, wherein the rate of change of phaseis controlled to reduce and eliminate audible side effects in thesystem.

13. The method as in paragraph 10, wherein the processing allows acontinuous streaming of data and update to the estimated location.

14. The method of paragraph 1 in which the acoustic field with codedphase is generated by at least one emitter.

15. The method of paragraph 1 in which the reflected acoustic wave issensed by at least one sensor.

16. The method in paragraph 9 in which the reference coded phase isknown with sufficient precision at the emitter that the locationcalculation yields sufficient accuracy.

17. The method in paragraph 9 in which the reference coded phase isextracted from the signal driving the emitter of the same referencecoded phase.

18. The method in paragraphs 2 and 10, wherein the phase coding in thesensed signal is extracted and compared to the reference phase coding inorder to calculate the distance of the reflector from the sensor.

19. The method in paragraphs 18, wherein the calculated distances fromeach sensed signal are combined to calculate a location of thereflector.

20. The method in paragraphs 2 and 10, wherein the sensed signals arecombined directly to calculate the differential phase in the coded phaseand therefore the differential distance travelled by the acoustic waveto each of the sensors.

21. The method in paragraphs 2 and 10, wherein the coding applied to thephase is sinusoidal modulation.

22. The method in paragraphs 2 and 10, wherein the coding applied to thephase is level coded and with a repeat distance more than twice themaximum distance that is to be sensed and calculated to avoid ambiguityin location of reflector.

(7). Camera Calibration Through Ultrasonic Range Sensing

1. Brightness Optimization

The brightness received at a camera location, as scattered light from anobject illuminated with infrared (IR) light, is thought to be dependenton the range of the tracked object from the source of light.

The 3D depth sensor system proposed herein consists of a source ofillumination, an optical camera in its proximity and an acoustictracking system. The latter consists of a set of transducers (up topossibly only one). They could be part of integrated haptic feedback,parametric audio, levitation systems, or a stand-alone tracking systemsupporting other applications. They could work in simplex or duplexmode, estimating the depth coordinates with traditional time-of-flightmeasurement.

FIG. 29 shows an exemplary 3D depth sensor system 2900 of this kind inwhich a LED emitter 2915 is chosen as source of light. The sensor system2900 also includes an object 2910, a photodiode 2920 and a transducer induplex mode 2930.

Given a source of IR light and an IR camera to its proximity, differentdata sets were collected during an experimental investigation to trainan algorithm correlating the amount of reflection of light from a handilluminated with IR, with its range from the source of light.Specifically, the depth coordinates of the center of the palm of onehand and the brightness of the pixel associated to the center of thepalm were extracted and collected for each measurement. This enabled theexploitation of the depth coordinate (i.e., the distance between thecenter of the camera and the palm center) as the ground truth (or targetvalue) to train the optimization algorithms.

Two experiments were run reducing the intensity of JR light emitted bythe LEDs and received by the camera, using two different types ofphotographic neutral-density (ND) filters, one low and one high filter.ND filters reduce the intensity of all the wavelengths of light at thecamera locations by the same factor. Hence, two different methods wereused to collect data:

1. By using an IR illuminator as an EM light source, with low ND filter

2. By using an IR illuminator as an EM light source, with high ND filter

Two training sets were collected during the experimental investigations.In method 1, there were 3516 training samples collected. In method 2,there were 4951 training samples collected.

The data is presented in FIG. 30, which shows a graph 3000 having anx-axis 3010 of brightness in uncalibrated units, a y-axis 3020 of depthin mm, a plot of method 1 3030 showing a parametric plot between the twoand a plot of method 2 3040 showing a parametric plot between the two.

Showing brightness versus depth, FIG. 30 displays a trend for whichdepth values are low when intensity values are high. Specifically, datashows a clear second order correlation between depth and brightness.When the high ND filter is used to reduce the intensity of the IRilluminator (method 2), brightness values associated to the same depthare lower. Specifically, the correlation between brightness and depthseems to undergo shifts in the x-axis for different values of lightintensities and reflectance of the tracked object.

FIG. 30 suggests the best fit for the training set to be asingle-variable, second order polynomial function, expressed as:

{circumflex over (f)}(x)=a·x ² +b·x+c  (28)

Where the single variable x is the brightness of the pixel associated tothe centre palm, and a, b and c are the coefficients of the polynomial.From {circumflex over (f)}(x), the range associated to other relevantfeatures of the hand (e.g. finger) can be estimated from theirrespective brightness values.

The data can be labelled with the index n=1, . . . , N, so that eachdata point consists of a value of x, denoted by x^(n), and acorresponding desired value for the output {circumflex over (f)}(x),denoted by t^(n), with w being a vector of polynomial coefficients. Inorder to find suitable values for the coefficients of the polynomial, itis commonly considered the error between the desired output t^(n), for aparticular input x^(n), and the corresponding value predicted by thepolynomial function given by f(x^(n); w). The least-square regressioninvolves minimizing the square of the error, summed over all data point,as follows:

$\begin{matrix}{E = {\frac{1}{2}{\sum\limits_{n = 1}^{N}\left\{ {{f\left( {x^{n};w} \right)} - t^{n}} \right\}^{2}}}} & (29)\end{matrix}$

Least-square optimization leads to polynomial curve fitting with acoefficient of determination (R-squared) equal to 0.975 and 0.959 formethod 1 and method 2. FIG. 31A shows the scatter plot 3100 of thegoodness of fit of the training samples obtained with the data ofmethod 1. The x-axis 3110 is yhat in mm, the y-axis 3120 is y in mm andplot 3130 shows goodness of fit. FIG. 31B shows the scatter plot 3150 ofthe goodness of fit of the training samples obtained with the data ofmethod 2. The x-axis 3160 is yhat in mm, the y-axis 3170 is y in mm andplot 3180 shows goodness of fit. In these graphs, y is “ground truth,”that is, real distance data, and yhat is the estimated value of distancef(x), where x is brightness.

The estimator {circumflex over (f)}(x) is subject to a few assumptionsand limitations. By hypothesis, it is assumed to be only dependent onbrightness, which seems a reasonable statement considering the highvalue of the coefficient of determination.

II. Fusion System and Calibration

The estimator {circumflex over (f)}(x) is also environment sensitive,especially to ambient IR lighting conditions and to reflectance of thetracked object. In fact, the use of different ND filters is likely tohorizontally shift the brightness values, preserving the overallrelationship with depth, as can be observed in FIG. 30. Hence, acontinuous calibration of the algorithm is needed to correctly shift thesecond order polynomial based on the lighting level and the reflectanceof the tracked object. This can be achieved if the range of relevantfeatures of the tracked objects are known. Consider an acoustic trackingsystem as an example scenario. If the target range of the center of massof the tracked object over time, t, is known, the equation (1) becomes aquadratic equation to be solved in terms of the correction factor K,when {circumflex over (f)}(x) is equal to t:

{circumflex over (f)}(x)=a(x−K)^(z) +b(x−K)+c  (30)

Where x is the brightness of the pixel associated to the centre of massof the tracked object (e.g. the center of a hand's palm) and the sign ofK is dictated by the sign of the difference between t and {circumflexover (f)}(x). The fusion between optical and acoustic data happening atthis stage contributes to the definition of an accurate tracking depthsystem and depth algorithm. The range of other relevant hand's features(i.e. fingers) can be estimated with equation (3), once the correctionfactor has been assessed. Since the brightness of other relevantfeatures of a hand would stay in a neighborhood of x, the consistentcalibration of equation (3) throughout time assures the estimation notto be divergent from the real value. Calibration can be performed atfixed time intervals.

FIG. 32 is a schematic 3200 the steps required in the 3D depth sensorsystem based on the camera calibration through ultrasonic range sensingproposed herein. Calibration data 3255 is fed to the start module 3205,which is then split into ultrasound 3210, illumination 3215 and a singleEM camera 3220. Using range measurement with ToF techniques theultrasound 3210 is processed using the absolute z-coordinate of therelevant feature 3245 and then fed to the fusion module 3250. Theillumination 3215 and a single EM camera 3220 merge and are: (i)processed by a brightness module 3255, an optimization algorithm 3230and the relative z-coordinate of the relevant feature 3235; and (ii)then fed to the fusion module 3250. The fusion module undergoescalibration 3255 and then is combined with x-coordinate, y-coordinatedata 3240 from the single EM camera 3220 to form x, y, z coordinates ofother relevant features 3260.

III. Further Disclosure

The estimator f(x) is also expected to be source and/or camera sensorspecific. In fact, the use of a different source (or a different camera,or both) would change also the coefficients of the polynomial, leadingto slightly different relationships. The effect of using a differentsource of IR light on the correlation between depth and brightness isshown in FIG. 33. FIG. 33 shows a graph 3300 having an x-axis 3310 ofbrightness in uncalibrated units, a y-axis 3320 of depth in mm, a plotof new LED 1 3330 showing the interface between the two and a plot ofnew LED 2 3340 showing the interface between the two. In this case asimple calibration would not help, but a new optimization algorithmshould be trained and adjusted based on a collection of new, camera orsource specific, training samples.

1. A 3D depth sensor system based on brightness optimization, collectedwith one single optical camera and calibrated exploiting withultrasonic, time-of-flight measurements.

A system of paragraph 1, which uses a second order polynomialoptimization algorithm to estimate range from brightness.

A system of paragraph 1, which is consistently calibrated with anacoustic tracking system at fixed update rates.

(8). Conclusion

While the foregoing descriptions disclose specific values, any otherspecific values may be used to achieve similar results. Further, thevarious features of the foregoing embodiments may be selected andcombined to produce numerous variations of improved haptic systems.

In the foregoing specification, specific embodiments have beendescribed. However, one of ordinary skill in the art appreciates thatvarious modifications and changes can be made without departing from thescope of the invention as set forth in the claims below. Accordingly,the specification and figures are to be regarded in an illustrativerather than a restrictive sense, and all such modifications are intendedto be included within the scope of present teachings.

Moreover, in this document, relational terms such as first and second,top and bottom, and the like may be used solely to distinguish oneentity or action from another entity or action without necessarilyrequiring or implying any actual such relationship or order between suchentities or actions. The terms “comprises,” “comprising,” “has”,“having,” “includes”, “including,” “contains”, “containing” or any othervariation thereof, are intended to cover a non-exclusive inclusion, suchthat a process, method, article, or apparatus that comprises, has,includes, contains a list of elements does not include only thoseelements but may include other elements not expressly listed or inherentto such process, method, article, or apparatus. An element proceeded by“comprises . . . a”, “has . . . a”, “includes . . . a”, “contains . . .a” does not, without more constraints, preclude the existence ofadditional identical elements in the process, method, article, orapparatus that comprises, has, includes, contains the element. The terms“a” and “an” are defined as one or more unless explicitly statedotherwise herein. The terms “substantially”, “essentially”,“approximately”, “about” or any other version thereof, are defined asbeing close to as understood by one of ordinary skill in the art. Theterm “coupled” as used herein is defined as connected, although notnecessarily directly and not necessarily mechanically. A device orstructure that is “configured” in a certain way is configured in atleast that way but may also be configured in ways that are not listed.

The Abstract of the Disclosure is provided to allow the reader toquickly ascertain the nature of the technical disclosure. It issubmitted with the understanding that it will not be used to interpretor limit the scope or meaning of the claims. In addition, in theforegoing Detailed Description, it can be seen that various features aregrouped together in various embodiments for the purpose of streamliningthe disclosure. This method of disclosure is not to be interpreted asreflecting an intention that the claimed embodiments require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive subject matter lies in less than allfeatures of a single disclosed embodiment. Thus, the following claimsare hereby incorporated into the Detailed Description, with each claimstanding on its own as a separately claimed subject matter.

1. A method comprising: using an acoustic transducer to track an objectby repeated switching between a control point activation state and aplane wave activation state; wherein the acoustic transducer contributesto formation of a control point during the control point activationstate; and wherein the acoustic transducer projects in-phase pulsedsignals during the plane wave activation state.
 2. The method of claim1, further comprising: modulating by interpolating arbitrary waveformsbetween the control point activation state and the plane wave activationstate so as to substantially maximize amplitude at the control point. 3.The method of claim 2, wherein the modulating is dynamically tailored tomatch a sensing objective and environment.
 4. A method comprising:removing a substantial source of noise in an acoustic signal undergoingamplitude modulation generated by a transducer by: a. adding phasechanges to alter the acoustic signal undergoing amplitude modulation;and b. finding points in the amplitude modulation where a change inamplitude induced by the phase changes substantially mimics a portion ofthe acoustic signal undergoing amplitude modulation.
 5. The method ofclaim 4, wherein the phase changes are separately detectable.
 6. Themethod of claim 4, wherein one of the phase changes is added before theminimum portion of the acoustic signal.
 7. A method comprising: inducinga phase singularity into each of a plurality of acoustic transducers,wherein each of the plurality of acoustic transducers has an originalphase shift; moving, in a first movement, each of the plurality oftransducers to generate an aggregate singularity pulse that produces ahaptic effect; and moving, in a second movement, the phase of each ofthe plurality of transducers back to the original phase shift of each ofthe plurality of transducers; wherein the first movement is faster thanthe second movement.
 8. The method of claim 7, wherein inducing a phasesingularity comprises encoding a sequence of auto-correlationmaximization symbols.
 9. The method as in claim 8, wherein encoding asequence of auto-correlation maximization symbols comprises assigning asymbol from the sequence of auto-correlation maximization symbols toeach of the plurality of acoustic transducers.
 10. A method comprising:tracking an object in a time-of-flight sensor fusion system using aplurality of acoustic transducers; integrating object-location data fromthe acoustic transducers and at least one optical camera, wherein thedata from the at least one optical camera provides spatial constraintsto the object-location data.
 11. The method as in claim 1, wherein theobject is a human hand and further comprising: using the at least oneoptical camera to recognize a hand and output the location and topologyof the hand in its projection plane.
 12. The method as in claim 1,further comprising: using trilateration based on time of arrival toestimate a position of the center of mass of the object with respect tothe plurality of acoustic transducers.
 13. A method comprising: trackinga location of an object by: a. generating spherical phase wave-fronts byan array of acoustic transducers within different amplitude modulatedwave-fronts; and b. tracking signals of the spherical phase wave-frontsreflected from the object using echo processing techniques.
 14. Themethod as in claim 13, further comprising: c. interpolating focusedstates to create focused regions of acoustic power and track the objectsimultaneously.
 15. The method as in claim 13, further comprising: d.tailoring modulation parameters dynamically to match a sensing objectiveand environment.
 16. A method comprising: generating an acoustic fieldhaving a carrier wavelength with known phase modulation having a phasemodulation wavelength; sensing acoustic energy reflected from an object;converting the acoustic energy into electrical signals; processing theelectrical signals to determine location of the object in the volume,wherein the phase modulation wavelength is long when compared to thecarrier wavelength.
 17. The method as in claim 1, wherein the electricalsignals are digitized for subsequent processing.
 18. The method as inclaim 1, wherein the acoustic field comprises a coded phase generated byat least one emitter.
 19. A system comprising: a 3D depth sensor systemcomprising: an illumination source, an acoustic tracking systemcomprising at least one acoustic transducer; an optical camera inproximity to the illumination source; wherein the optical camera iscalibrated based on brightness optimization, collected with the opticalcamera and calibrated with time-of-flight measurements from the acoustictracking system.
 20. The system as in claim 19, wherein the brightnessoptimization uses a second order polynomial optimization algorithm toestimate range from brightness.
 21. (canceled)